{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\outputs\logs\twfe_att_es.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}19 May 2024, 20:21:55
{txt}
{com}. 
. ********************************************************************************
. ***************** Flood to Center Pivot or LEPA ********************************
. ********************************************************************************
. use "$dr_temp\wrg_collapsed.dta", replace
{txt}
{com}.         *Check how many WR_GROUPS are missing aquifer characteristics
.         xtset WR_GROUP wua_year
{res}
{col 1}{txt:Panel variable: }{res:WR_GROUP}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:wua_year}{txt:, }{res:{bind:1991}}{txt: to }{res:{bind:2019}}{txt:, but with gaps}{p_end}
{txt}{col 10}Delta: {res}1 unit
{txt}
{com}. 
. /* Address missing and extreme values */
.                 *Drop wr_groups who don't have predevelopment characteristics
.                 keep if !missing(sy)
{txt}(70,993 observations deleted)

{com}.                 keep if !missing(predev_sat)
{txt}(0 observations deleted)

{com}.                 keep if !missing(predev_dtw)
{txt}(0 observations deleted)

{com}.                 keep if !missing(hyd_cond)
{txt}(0 observations deleted)

{com}.                 *Drop wr_groups if they have missing precip or soil data
.                 drop if missing(jan_april_mean_ppt)
{txt}(0 observations deleted)

{com}.                 drop if missing(awc)
{txt}(885 observations deleted)

{com}.                 *Drop wr_groups if they have missing authorized quantities or acreage
.                 drop if missing(wrg_authquant_IRR_umw) | missing(wrg_authquant_all_umw) | missing(wrg_auth_irr_acres)
{txt}(8,674 observations deleted)

{com}.                 *Drop observations with recorded withdrawals but no irrigated acres and vice-versa
.                 drop if af_used_irr>0 & acres_irr==0
{txt}(495 observations deleted)

{com}.                 drop if acres_irr>0 & af_used_irr==0
{txt}(0 observations deleted)

{com}.                 *Create a irrigation intensity outcome variable
.                 bysort WR_GROUP wua_year: gen depth_applied = af_used_irr/acres_irr
{txt}(6,023 missing values generated)

{com}.                 *Drop extreme values for af_used, acres_irr 
.                         sum af_used, d

                        {txt}(sum) af_used
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}       19              0
{txt}10%    {res}       44              0       {txt}Obs         {res}    363,631
{txt}25%    {res}       96              0       {txt}Sum of wgt. {res}    363,631

{txt}50%    {res}      161                      {txt}Mean          {res} 243.0458
                        {txt}Largest       Std. dev.     {res}  362.545
{txt}75%    {res}      264       15725.45
{txt}90%    {res}   500.84       16005.28       {txt}Variance      {res} 131438.9
{txt}95%    {res}      729       16191.03       {txt}Skewness      {res} 13.12807
{txt}99%    {res}  1431.82        18592.3       {txt}Kurtosis      {res} 365.9421
{txt}
{com}.                                 drop if af_used >= r(p99) & af_used < .
{txt}(3,637 observations deleted)

{com}.                         sum acres_irr, d

                       {txt}(sum) acres_irr
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}       40              0
{txt}10%    {res}       73              0       {txt}Obs         {res}    359,994
{txt}25%    {res}      120              0       {txt}Sum of wgt. {res}    359,994

{txt}50%    {res}      130                      {txt}Mean          {res} 197.6613
                        {txt}Largest       Std. dev.     {res}   176.09
{txt}75%    {res}      240           3540
{txt}90%    {res}      390           3780       {txt}Variance      {res}  31007.7
{txt}95%    {res}      523           4000       {txt}Skewness      {res} 3.452098
{txt}99%    {res}      911           5120       {txt}Kurtosis      {res} 25.24593
{txt}
{com}.                                 drop if acres_irr >= r(p99) & acres_irr < .
{txt}(3,605 observations deleted)

{com}.                         sum depth_applied, d

                        {txt}depth_applied
{hline 61}
      Percentiles      Smallest
 1%    {res}       .1        .000069
{txt} 5%    {res} .3129231       .0000769
{txt}10%    {res}     .472         .00008       {txt}Obs         {res}    350,366
{txt}25%    {res} .7586667       .0000833       {txt}Sum of wgt. {res}    350,366

{txt}50%    {res} 1.097462                      {txt}Mean          {res} 1.123839
                        {txt}Largest       Std. dev.     {res} .5311018
{txt}75%    {res} 1.449077          16.57
{txt}90%    {res} 1.799385         17.415       {txt}Variance      {res} .2820692
{txt}95%    {res}        2          27.87       {txt}Skewness      {res} 1.816609
{txt}99%    {res}    2.475          29.83       {txt}Kurtosis      {res} 57.22109
{txt}
{com}.                                 drop if depth_applied >= r(p99) & depth_applied < .     
{txt}(3,513 observations deleted)

{com}. 
. /* Address technologies outside study, multiple adopters and reverters */
.         *Drop wr_groups that don't have systems codes we're interested in *
.                 drop if type_system_2 > 0 | type_system_5 > 0 | type_system_6 > 0 | ///
>                         type_system_7 > 0 | type_system_8 > 0 | type_system_9 > 0
{txt}(37,437 observations deleted)

{com}.         *Generate indicator for cp or lepa
.                 gen type_system_3_4 = type_system_3==1 | type_system_4==1
{txt}
{com}.         *Check how many groups switch into each final system type multiple times
.                 gen switch_flood_cplepa = l.type_system_1==1 & type_system_3_4==1
{txt}
{com}.                         by WR_GROUP: egen num_switch_flood_cplepa = total(switch_flood_cplepa)
{txt}
{com}.         *Create variables showing reversion from one to another, and how many times they revert
.                 gen reversion_cplepa_flood = l.type_system_3_4==1 & type_system_1==1
{txt}
{com}.                         by WR_GROUP: egen num_revers_cplepa_flood = total(reversion_cplepa_flood)
{txt}
{com}.         *Get rid of multiple switchers or reverters
.                 drop if num_switch_flood_cplepa>=2
{txt}(4,377 observations deleted)

{com}.                 drop if num_revers_cplepa_flood>=1
{txt}(12,378 observations deleted)

{com}.         *Create variable with gmd number
.                 gen gmd = 0
{txt}
{com}.                 forvalues i=1(1)5 {c -(}
{txt}  2{com}.                         replace gmd = `i' if gmd_`i'==1
{txt}  3{com}.                 {c )-}
{txt}(18,928 real changes made)
(24,224 real changes made)
(98,511 real changes made)
(51,933 real changes made)
(86,546 real changes made)

{com}.         *Drop groups with fractional system_codes
.                 foreach var of varlist type_system_1 type_system_3 type_system_4 {c -(}
{txt}  2{com}.                         keep if `var'==1 | `var'==0
{txt}  3{com}.                 {c )-}
{txt}(5,407 observations deleted)
(1,898 observations deleted)
(0 observations deleted)

{com}.         
. /* Label variables */
.         label var wua_year "Year"
{txt}
{com}.         label var af_used "Acre-feet applied"
{txt}
{com}.         label var acres_irr "Irrigated acres"
{txt}
{com}.         label var depth_applied "Depth applied - acre-feet applied per acre"
{txt}
{com}.         label var awc "Available water capacity"
{txt}
{com}.         label var sandtotal "Sand content (%)"
{txt}
{com}.         label var silttotal "Silt content (%)"
{txt}
{com}.         label var slope "Slope"
{txt}
{com}.         label var jan_april_mean_ppt "Preseason precipitation (mm)"
{txt}
{com}.         label var may_sep_mean_ppt "Growing season precipitation (mm)"
{txt}
{com}.         label var jan_april_mean_ET0_elev "Preseason evapotranspiration, using elevation from ssurgo"
{txt}
{com}.         label var may_sep_mean_ET0_elev "Growing season evapotranspiration, using elevation from ssurgo"
{txt}
{com}.         label var jan_april_mean_ET0_Har "Preseason evapotranspiration"
{txt}
{com}.         label var may_sep_mean_ET0_Har "Growing season evapotranspiration"
{txt}
{com}. 
. ***************** Creation of treatment and cohort variables *******************
. distinct(WR_GROUP) if num_switch_flood_cplepa==1 /*1660 WR_GROUPs with an observed switch from flood to cp or lepa */

{txt}{col 11}{c |}        Observations
{col 11}{c |}      total   distinct
{hline 10}{c +}{hline 22}
 WR_GROUP {c |}  {res}    41440       1660
{txt}
{com}. distinct WR_GROUP  /* still at 12,922 wrgs */

{txt}{col 11}{c |}        Observations
{col 11}{c |}      total   distinct
{hline 10}{c +}{hline 22}
 WR_GROUP {c |}  {res}   291379      12922
{txt}
{com}. 
. /*Check how many are switches from flood to cp versus flood straight to LEPA */
.         gen switch_flood_cp = l.type_system_1==1 & type_system_3==1
{txt}
{com}.                 by WR_GROUP: egen num_switch_flood_cp = total(switch_flood_cp)
{txt}
{com}.         gen switch_flood_lepa = l.type_system_1==1 & type_system_4==1
{txt}
{com}.                 by WR_GROUP: egen num_switch_flood_lepa = total(switch_flood_lepa)
{txt}
{com}.         *Isolate true controls and not-yet-treated units
.         bysort WR_GROUP: egen any_flood = max(type_system_1)
{txt}
{com}.         bysort WR_GROUP: egen any_cp_or_lepa = max(type_system_3_4)
{txt}
{com}.                 *Check to see if we have any cp or lepa users who we don't observe the switch for
.                 tab any_cp_or_lepa if num_switch_flood_cplepa==0 

{txt}any_cp_or_l {c |}
        epa {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}     16,578        6.63        6.63
{txt}          1 {c |}{res}    233,361       93.37      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}    249,939      100.00
{txt}
{com}.                         *DROP THEM
.                         drop if any_cp_or_lepa==1 & num_switch_flood_cplepa==0 
{txt}(233,361 observations deleted)

{com}.                         distinct WR_GROUP /*This brings us down to 2817 */

{txt}{col 11}{c |}        Observations
{col 11}{c |}      total   distinct
{hline 10}{c +}{hline 22}
 WR_GROUP {c |}  {res}    58018       2817
{txt}
{com}.                                 /* But remember this switch variable is constructed based on 
>                                 seeing a switch between consecutive years */
.                 *Check to see if we have lepa obervations for people who changed in 1990
.                 tab any_flood if any_cp_or_lepa==1  /*We don't have any */

  {txt}any_flood {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}     41,440      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     41,440      100.00
{txt}
{com}.                 distinct(WR_GROUP) if any_flood==0 & any_cp_or_lepa==1

{txt}{col 11}{c |}        Observations
{col 11}{c |}      total   distinct
{hline 10}{c +}{hline 22}
 WR_GROUP {c |}  {res}        0          0
{txt}
{com}.                         *Drop them if they existed
.                         drop if any_flood==0 & any_cp_or_lepa==1
{txt}(0 observations deleted)

{com}.                         
.         *Now check to see how many remain
.         distinct(WR_GROUP) /* 2817 water right groups in total */

{txt}{col 11}{c |}        Observations
{col 11}{c |}      total   distinct
{hline 10}{c +}{hline 22}
 WR_GROUP {c |}  {res}    58018       2817
{txt}
{com}.         distinct(WR_GROUP) if num_switch_flood_cplepa==0 /*   1157 never treated water right groups in total */

{txt}{col 11}{c |}        Observations
{col 11}{c |}      total   distinct
{hline 10}{c +}{hline 22}
 WR_GROUP {c |}  {res}    16578       1157
{txt}
{com}.                 tab type_system_1 if num_switch_flood_cplepa==0 /* All of which are never-treated and only use flood */

     {txt}(mean) {c |}
type_system {c |}
         _1 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}     16,578      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     16,578      100.00
{txt}
{com}.         distinct(WR_GROUP) if num_switch_flood_cplepa==1 /* 1,660 water right groups make a switch at some point */

{txt}{col 11}{c |}        Observations
{col 11}{c |}      total   distinct
{hline 10}{c +}{hline 22}
 WR_GROUP {c |}  {res}    41440       1660
{txt}
{com}.         
.         *Create variable for first year adopting cp or lepa from flood to be the cohort year
.                 tab switch_flood_cplepa wua_year

{txt}switch_flo {c |}                                                                Year
 od_cplepa {c |}      1991       1992       1993       1994       1995       1996       1997       1998       1999       2000       2001       2002 {c |}     Total
{hline 11}{c +}{hline 132}{c +}{hline 10}
         0 {c |}{res}     2,346      2,185      2,147      2,237      2,163      2,146      2,127      2,034      2,100      2,066      1,985      1,945 {txt}{c |}{res}    56,358 
{txt}         1 {c |}{res}         0        129         97        140        148        134        168        141         67         80         72         56 {txt}{c |}{res}     1,660 
{txt}{hline 11}{c +}{hline 132}{c +}{hline 10}
     Total {c |}{res}     2,346      2,314      2,244      2,377      2,311      2,280      2,295      2,175      2,167      2,146      2,057      2,001 {txt}{c |}{res}    58,018 


{txt}switch_flo {c |}                                                                Year
 od_cplepa {c |}      2003       2004       2005       2006       2007       2008       2009       2010       2011       2012       2013       2014 {c |}     Total
{hline 11}{c +}{hline 132}{c +}{hline 10}
         0 {c |}{res}     1,949      1,925      1,957      1,914      1,887      1,895      1,891      1,899      1,797      1,746      1,740      1,774 {txt}{c |}{res}    56,358 
{txt}         1 {c |}{res}        47         49         33         20         28         29         23         16         43         42         35         19 {txt}{c |}{res}     1,660 
{txt}{hline 11}{c +}{hline 132}{c +}{hline 10}
     Total {c |}{res}     1,996      1,974      1,990      1,934      1,915      1,924      1,914      1,915      1,840      1,788      1,775      1,793 {txt}{c |}{res}    58,018 


{txt}switch_flo {c |}                          Year
 od_cplepa {c |}      2015       2016       2017       2018       2019 {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}     1,710      1,723      1,721      1,707      1,642 {txt}{c |}{res}    56,358 
{txt}         1 {c |}{res}        12         12          5          8          7 {txt}{c |}{res}     1,660 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}     1,722      1,735      1,726      1,715      1,649 {txt}{c |}{res}    58,018 
{txt}
{com}.                 gen switch_year_flood_cplepa = switch_flood_cplepa*wua_year
{txt}
{com}.                 tab switch_year_flood_cplepa if switch_year_flood_cplepa!=0 

{txt}switch_year {c |}
_flood_cple {c |}
         pa {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       1992 {c |}{res}        129        7.77        7.77
{txt}       1993 {c |}{res}         97        5.84       13.61
{txt}       1994 {c |}{res}        140        8.43       22.05
{txt}       1995 {c |}{res}        148        8.92       30.96
{txt}       1996 {c |}{res}        134        8.07       39.04
{txt}       1997 {c |}{res}        168       10.12       49.16
{txt}       1998 {c |}{res}        141        8.49       57.65
{txt}       1999 {c |}{res}         67        4.04       61.69
{txt}       2000 {c |}{res}         80        4.82       66.51
{txt}       2001 {c |}{res}         72        4.34       70.84
{txt}       2002 {c |}{res}         56        3.37       74.22
{txt}       2003 {c |}{res}         47        2.83       77.05
{txt}       2004 {c |}{res}         49        2.95       80.00
{txt}       2005 {c |}{res}         33        1.99       81.99
{txt}       2006 {c |}{res}         20        1.20       83.19
{txt}       2007 {c |}{res}         28        1.69       84.88
{txt}       2008 {c |}{res}         29        1.75       86.63
{txt}       2009 {c |}{res}         23        1.39       88.01
{txt}       2010 {c |}{res}         16        0.96       88.98
{txt}       2011 {c |}{res}         43        2.59       91.57
{txt}       2012 {c |}{res}         42        2.53       94.10
{txt}       2013 {c |}{res}         35        2.11       96.20
{txt}       2014 {c |}{res}         19        1.14       97.35
{txt}       2015 {c |}{res}         12        0.72       98.07
{txt}       2016 {c |}{res}         12        0.72       98.80
{txt}       2017 {c |}{res}          5        0.30       99.10
{txt}       2018 {c |}{res}          8        0.48       99.58
{txt}       2019 {c |}{res}          7        0.42      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,660      100.00
{txt}
{com}.                 by WR_GROUP: egen cohort_flood_cplepa = max(switch_year_flood_cplepa), missing
{txt}
{com}.                 tab cohort_flood_cplepa, m

{txt}cohort_floo {c |}
   d_cplepa {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}     16,578       28.57       28.57
{txt}       1992 {c |}{res}      3,388        5.84       34.41
{txt}       1993 {c |}{res}      2,326        4.01       38.42
{txt}       1994 {c |}{res}      3,464        5.97       44.39
{txt}       1995 {c |}{res}      3,626        6.25       50.64
{txt}       1996 {c |}{res}      3,446        5.94       56.58
{txt}       1997 {c |}{res}      4,227        7.29       63.87
{txt}       1998 {c |}{res}      3,494        6.02       69.89
{txt}       1999 {c |}{res}      1,646        2.84       72.73
{txt}       2000 {c |}{res}      2,004        3.45       76.18
{txt}       2001 {c |}{res}      1,799        3.10       79.28
{txt}       2002 {c |}{res}      1,452        2.50       81.78
{txt}       2003 {c |}{res}      1,181        2.04       83.82
{txt}       2004 {c |}{res}      1,245        2.15       85.97
{txt}       2005 {c |}{res}        832        1.43       87.40
{txt}       2006 {c |}{res}        473        0.82       88.22
{txt}       2007 {c |}{res}        728        1.25       89.47
{txt}       2008 {c |}{res}        661        1.14       90.61
{txt}       2009 {c |}{res}        580        1.00       91.61
{txt}       2010 {c |}{res}        404        0.70       92.31
{txt}       2011 {c |}{res}      1,104        1.90       94.21
{txt}       2012 {c |}{res}      1,037        1.79       96.00
{txt}       2013 {c |}{res}        867        1.49       97.49
{txt}       2014 {c |}{res}        464        0.80       98.29
{txt}       2015 {c |}{res}        283        0.49       98.78
{txt}       2016 {c |}{res}        214        0.37       99.15
{txt}       2017 {c |}{res}        123        0.21       99.36
{txt}       2018 {c |}{res}        197        0.34       99.70
{txt}       2019 {c |}{res}        175        0.30      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     58,018      100.00
{txt}
{com}.                 tab cohort_flood_cplepa if cohort_flood_cplepa!=0

{txt}cohort_floo {c |}
   d_cplepa {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       1992 {c |}{res}      3,388        8.18        8.18
{txt}       1993 {c |}{res}      2,326        5.61       13.79
{txt}       1994 {c |}{res}      3,464        8.36       22.15
{txt}       1995 {c |}{res}      3,626        8.75       30.90
{txt}       1996 {c |}{res}      3,446        8.32       39.21
{txt}       1997 {c |}{res}      4,227       10.20       49.41
{txt}       1998 {c |}{res}      3,494        8.43       57.85
{txt}       1999 {c |}{res}      1,646        3.97       61.82
{txt}       2000 {c |}{res}      2,004        4.84       66.65
{txt}       2001 {c |}{res}      1,799        4.34       70.99
{txt}       2002 {c |}{res}      1,452        3.50       74.50
{txt}       2003 {c |}{res}      1,181        2.85       77.35
{txt}       2004 {c |}{res}      1,245        3.00       80.35
{txt}       2005 {c |}{res}        832        2.01       82.36
{txt}       2006 {c |}{res}        473        1.14       83.50
{txt}       2007 {c |}{res}        728        1.76       85.26
{txt}       2008 {c |}{res}        661        1.60       86.85
{txt}       2009 {c |}{res}        580        1.40       88.25
{txt}       2010 {c |}{res}        404        0.97       89.23
{txt}       2011 {c |}{res}      1,104        2.66       91.89
{txt}       2012 {c |}{res}      1,037        2.50       94.39
{txt}       2013 {c |}{res}        867        2.09       96.49
{txt}       2014 {c |}{res}        464        1.12       97.61
{txt}       2015 {c |}{res}        283        0.68       98.29
{txt}       2016 {c |}{res}        214        0.52       98.81
{txt}       2017 {c |}{res}        123        0.30       99.10
{txt}       2018 {c |}{res}        197        0.48       99.58
{txt}       2019 {c |}{res}        175        0.42      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     41,440      100.00
{txt}
{com}.                 *Check remaining dataset
.                 xtdescribe 

{txt}WR_GROUP:  {res}3{txt}, {res}4{txt}, ..., {res}21597                                  {txt}n ={res}       2817
{txt}wua_year:  {res}1991, 1992, ..., 2019                             {txt}T ={res}         29
           {txt}Delta(wua_year) = {res}1 unit
           {txt}Span(wua_year)  = {res}29 periods
           {txt}(WR_GROUP*wua_year uniquely identifies each observation)

Distribution of T_i:   min      5%     25%       50%       75%     95%     max
                    {res}     1       3      14        24        28      29      29

{txt}{col 6}Freq.  Percent    Cum. {c |}  Pattern
 {hline 27}{c +}{c -}{c -}{hline 29}
{res}      478     16.97   16.97{txt} {c |}  {res}11111111111111111111111111111
       42      1.49   18.46{txt} {c |}  {res}.1111111111111111111111111111
       33      1.17   19.63{txt} {c |}  {res}1.111111111111111111111111111
       32      1.14   20.77{txt} {c |}  {res}11.11111111111111111111111111
       28      0.99   21.76{txt} {c |}  {res}11...........................
       25      0.89   22.65{txt} {c |}  {res}1............................
       22      0.78   23.43{txt} {c |}  {res}11111111111.11111111111111111
       19      0.67   24.10{txt} {c |}  {res}1111.........................
       19      0.67   24.78{txt} {c |}  {res}1111111111111111111111111111.
     2119     75.22  100.00{txt} {c |} (other patterns)
 {hline 27}{c +}{c -}{c -}{hline 29}
{res}     2817    100.00        {txt} {c |}  {res}XXXXXXXXXXXXXXXXXXXXXXXXXXXXX
{txt}
{com}.                 
.         *Drop observations where they switched backed to flood from cp or lepa but we didn't observe it
.                 tab type_system_1 if wua_year>=cohort_flood_cplepa & cohort_flood_cplepa!=0 /*There are 79 of these observations */

     {txt}(mean) {c |}
type_system {c |}
         _1 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}     29,058       99.73       99.73
{txt}          1 {c |}{res}         79        0.27      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     29,137      100.00
{txt}
{com}.                         gen false_nyt = 0
{txt}
{com}.                         replace false_nyt = 1 if type_system_1==1 & cohort_flood_cplepa!=0 & wua_year>=cohort_flood_cplepa
{txt}(79 real changes made)

{com}.                         bysort WR_GROUP: egen false_contr = max(false_nyt)      
{txt}
{com}.                         drop if false_contr==1
{txt}(533 observations deleted)

{com}. 
.         *Check how many make the switch and when
.                 preserve
{txt}
{com}.                 keep WR_GROUP cohort_flood_cplepa
{txt}
{com}.                 duplicates drop

{p 0 4}{txt}Duplicates in terms of {txt} all variables{p_end}

(54,700 observations deleted)

{com}.                 tab cohort_flood_cplepa if cohort_flood_cplepa!=0

{txt}cohort_floo {c |}
   d_cplepa {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       1992 {c |}{res}        127        7.80        7.80
{txt}       1993 {c |}{res}         94        5.77       13.57
{txt}       1994 {c |}{res}        136        8.35       21.93
{txt}       1995 {c |}{res}        144        8.85       30.77
{txt}       1996 {c |}{res}        132        8.11       38.88
{txt}       1997 {c |}{res}        164       10.07       48.96
{txt}       1998 {c |}{res}        139        8.54       57.49
{txt}       1999 {c |}{res}         67        4.12       61.61
{txt}       2000 {c |}{res}         79        4.85       66.46
{txt}       2001 {c |}{res}         70        4.30       70.76
{txt}       2002 {c |}{res}         56        3.44       74.20
{txt}       2003 {c |}{res}         45        2.76       76.97
{txt}       2004 {c |}{res}         48        2.95       79.91
{txt}       2005 {c |}{res}         33        2.03       81.94
{txt}       2006 {c |}{res}         19        1.17       83.11
{txt}       2007 {c |}{res}         27        1.66       84.77
{txt}       2008 {c |}{res}         29        1.78       86.55
{txt}       2009 {c |}{res}         23        1.41       87.96
{txt}       2010 {c |}{res}         16        0.98       88.94
{txt}       2011 {c |}{res}         42        2.58       91.52
{txt}       2012 {c |}{res}         42        2.58       94.10
{txt}       2013 {c |}{res}         34        2.09       96.19
{txt}       2014 {c |}{res}         19        1.17       97.36
{txt}       2015 {c |}{res}         11        0.68       98.03
{txt}       2016 {c |}{res}         12        0.74       98.77
{txt}       2017 {c |}{res}          5        0.31       99.08
{txt}       2018 {c |}{res}          8        0.49       99.57
{txt}       2019 {c |}{res}          7        0.43      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,628      100.00
{txt}
{com}.                 restore
{txt}
{com}.                 /* 1628 water right groups make the switch at some point */ 
. 
. ***************** Table 2 - Flood to cp or LEPA column *************************
. *Create indicator of treatment status, showing not-yet-treated and treated through time
.         gen treat_status = 0
{txt}
{com}.         replace treat_status = 1 if wua_year >= cohort_flood_cplepa & cohort_flood_cplepa!=0
{txt}(28,803 real changes made)

{com}.         distinct(WR_GROUP) if treat_status==1 & wua_year <= 2015 

{txt}{col 11}{c |}        Observations
{col 11}{c |}      total   distinct
{hline 10}{c +}{hline 22}
 WR_GROUP {c |}  {res}    23248       1596
{txt}
{com}.                 /* 1,596 of the 1,628 WR_GROUPs who make the switch from 
>                         flood to cp or lepa do so between 1991 and 2015*/
.         distinct(WR_GROUP) if cohort_flood_cplepa>2015 /* 32 nyt who adopt after 2015 */

{txt}{col 11}{c |}        Observations
{col 11}{c |}      total   distinct
{hline 10}{c +}{hline 22}
 WR_GROUP {c |}  {res}      709         32
{txt}
{com}.         distinct(WR_GROUP) /* 2,785 total wrgs */

{txt}{col 11}{c |}        Observations
{col 11}{c |}      total   distinct
{hline 10}{c +}{hline 22}
 WR_GROUP {c |}  {res}    57485       2785
{txt}
{com}.         distinct(WR_GROUP) if any_flood==1 & any_cp_or_lepa==0 /* 1,157 controls */

{txt}{col 11}{c |}        Observations
{col 11}{c |}      total   distinct
{hline 10}{c +}{hline 22}
 WR_GROUP {c |}  {res}    16578       1157
{txt}
{com}.         distinct(WR_GROUP) if any_flood==1 & any_cp_or_lepa==1 /* 1,628 treated */

{txt}{col 11}{c |}        Observations
{col 11}{c |}      total   distinct
{hline 10}{c +}{hline 22}
 WR_GROUP {c |}  {res}    40907       1628
{txt}
{com}. 
. /* Create new variable that is the acre-feet applied in the earliest year recorded for each WRG */
.         bysort WR_GROUP: egen min_year = min(wua_year)
{txt}
{com}.         bysort WR_GROUP: gen init_af_used = af_used_irr if min_year==wua_year
{txt}(54,700 missing values generated)

{com}.         bysort WR_GROUP: egen init_af_used_2 = min(init_af_used)
{txt}
{com}.         replace init_af_used = init_af_used_2
{txt}(54,700 real changes made)

{com}.         drop init_af_used_2
{txt}
{com}. 
.         //////////////////////////////////
>         ///// DROP NEVER TREATED /////////
>         //////////////////////////////////
>         keep if num_switch_flood_cplepa==1
{txt}(16,578 observations deleted)

{com}. 
. /* Export to perform Callaway and Sant'Anna estimation in R*/
. export delimited using "$dr_temp\flood_cporlepa_prepped.csv", replace
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\flood_cporlepa_prepped.csv} saved

{com}. 
. ********* Flood to CP or LEPA - TWFE estimates for Figures 3, 4, and 5 *********
. *Limit to 1991-2015
. keep if inrange(wua_year, 1991, 2015) 
{txt}(5,591 observations deleted)

{com}. xtset WR_GROUP wua_year
{res}
{col 1}{txt:Panel variable: }{res:WR_GROUP}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:wua_year}{txt:, }{res:{bind:1991}}{txt: to }{res:{bind:2015}}{txt:, but with gaps}{p_end}
{txt}{col 10}Delta: {res}1 unit
{txt}
{com}. xtdescribe

{txt}WR_GROUP:  {res}4{txt}, {res}10{txt}, ..., {res}21527                                 {txt}n ={res}       1628
{txt}wua_year:  {res}1991, 1992, ..., 2015                             {txt}T ={res}         25
           {txt}Delta(wua_year) = {res}1 unit
           {txt}Span(wua_year)  = {res}25 periods
           {txt}(WR_GROUP*wua_year uniquely identifies each observation)

Distribution of T_i:   min      5%     25%       50%       75%     95%     max
                    {res}     1      12      21        23        25      25      25

{txt}{col 6}Freq.  Percent    Cum. {c |}  Pattern
 {hline 27}{c +}{c -}{c -}{hline 25}
{res}      455     27.95   27.95{txt} {c |}  {res}1111111111111111111111111
       42      2.58   30.53{txt} {c |}  {res}.111111111111111111111111
       27      1.66   32.19{txt} {c |}  {res}11.1111111111111111111111
       22      1.35   33.54{txt} {c |}  {res}1.11111111111111111111111
       21      1.29   34.83{txt} {c |}  {res}11111111111.1111111111111
       20      1.23   36.06{txt} {c |}  {res}11111.1111111111111111111
       18      1.11   37.16{txt} {c |}  {res}1111111111111.11111111111
       17      1.04   38.21{txt} {c |}  {res}111.111111111111111111111
       16      0.98   39.19{txt} {c |}  {res}1111111111.11111111111111
      990     60.81  100.00{txt} {c |} (other patterns)
 {hline 27}{c +}{c -}{c -}{hline 25}
{res}     1628    100.00        {txt} {c |}  {res}XXXXXXXXXXXXXXXXXXXXXXXXX
{txt}
{com}.         putexcel set "$dr_temp\twfe.xlsx", ///
>                 sheet("floodtocplepa_91_15_nyt") modify
{res}{txt}
{com}.         putexcel A1 = "dep_var"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

{com}.         putexcel B1 = "effect estimated"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

{com}.         putexcel C1 = "estimate"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

{com}.         putexcel D1 = "se_estimate"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

{com}.         putexcel E1 = "lb_estimate"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

{com}.         putexcel F1 = "ub_estimate"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

{com}.         putexcel G1 = "panel_mean_dep_var"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

{com}.         putexcel H1 = "scaled_estimate"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

{com}.         putexcel I1 = "scaled_lb"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

{com}.         putexcel J1 = "scaled_ub"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

{com}.         local i = 2
{txt}
{com}.         foreach y in "af_used"  "acres_irr" "depth_applied" {c -(}
{txt}  2{com}.                 putexcel A`i' = "`y'"
{txt}  3{com}.                 putexcel B`i' = "effect_average"
{txt}  4{com}.                 sum `y'
{txt}  5{com}.                 local var_mean = r(mean)
{txt}  6{com}.                         putexcel G`i' = `var_mean'
{txt}  7{com}.                 xtreg `y' treat_status ///
>                         jan_april_mean_ppt may_sep_mean_ppt jan_april_mean_ET0_elev may_sep_mean_ET0_elev i.wua_year ///
>                         , fe vce(cluster WR_GROUP)
{txt}  8{com}.                 putexcel C`i' = _b[treat_status]
{txt}  9{com}.                 putexcel D`i' = _se[treat_status]
{txt} 10{com}.                 local ++i
{txt} 11{com}.         {c )-}
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}af_used {c |}{res}     35,316    192.0391    151.6318        .01    1404.87
{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved
{res}
{txt}Fixed-effects (within) regression{col 49}Number of obs{col 67}={col 69}{res}    33,953
{txt}Group variable: {res}WR_GROUP{txt}{col 49}Number of groups{col 67}={col 69}{res}     1,565

{txt}R-squared:{col 49}Obs per group:
     Within  = {res}0.1241{col 63}{txt}min{col 67}={col 69}{res}         1
{txt}     Between = {res}0.1976{col 63}{txt}avg{col 67}={col 69}{res}      21.7
{txt}     Overall = {res}0.1180{col 63}{txt}max{col 67}={col 69}{res}        25

{txt}{col 49}F({res}29{txt},{res}1564{txt}){col 67}={col 70}{res}    83.05
{txt}corr(u_i, Xb) = {res}0.1665{txt}{col 49}Prob > F{col 67}={col 73}{res}0.0000

{txt}{ralign 89:(Std. err. adjusted for {res:1,565} clusters in {res:WR_GROUP})}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                af_used{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}treat_status {c |}{col 25}{res}{space 2}-17.97149{col 37}{space 2} 2.529021{col 48}{space 1}   -7.11{col 57}{space 3}0.000{col 65}{space 4}-22.93212{col 78}{space 3}-13.01086
{txt}{space 5}jan_april_mean_ppt {c |}{col 25}{res}{space 2}-.4335476{col 37}{space 2} .0591029{col 48}{space 1}   -7.34{col 57}{space 3}0.000{col 65}{space 4}-.5494768{col 78}{space 3}-.3176184
{txt}{space 7}may_sep_mean_ppt {c |}{col 25}{res}{space 2}-.5804088{col 37}{space 2} .0370306{col 48}{space 1}  -15.67{col 57}{space 3}0.000{col 65}{space 4}-.6530437{col 78}{space 3} -.507774
{txt}jan_april_mean_ET0_elev {c |}{col 25}{res}{space 2} 44.05843{col 37}{space 2} 6.979692{col 48}{space 1}    6.31{col 57}{space 3}0.000{col 65}{space 4} 30.36789{col 78}{space 3} 57.74897
{txt}{space 2}may_sep_mean_ET0_elev {c |}{col 25}{res}{space 2} 2.976609{col 37}{space 2} 7.129046{col 48}{space 1}    0.42{col 57}{space 3}0.676{col 65}{space 4}-11.00689{col 78}{space 3}  16.9601
{txt}{space 23} {c |}
{space 15}wua_year {c |}
{space 18}1992  {c |}{col 25}{res}{space 2}-57.32344{col 37}{space 2} 4.020597{col 48}{space 1}  -14.26{col 57}{space 3}0.000{col 65}{space 4}-65.20977{col 78}{space 3}-49.43711
{txt}{space 18}1993  {c |}{col 25}{res}{space 2}-24.27012{col 37}{space 2} 7.108683{col 48}{space 1}   -3.41{col 57}{space 3}0.001{col 65}{space 4}-38.21367{col 78}{space 3}-10.32656
{txt}{space 18}1994  {c |}{col 25}{res}{space 2}-34.84913{col 37}{space 2} 3.457987{col 48}{space 1}  -10.08{col 57}{space 3}0.000{col 65}{space 4}-41.63191{col 78}{space 3}-28.06635
{txt}{space 18}1995  {c |}{col 25}{res}{space 2}-24.02735{col 37}{space 2} 4.444005{col 48}{space 1}   -5.41{col 57}{space 3}0.000{col 65}{space 4}-32.74419{col 78}{space 3}-15.31052
{txt}{space 18}1996  {c |}{col 25}{res}{space 2}-45.55381{col 37}{space 2} 4.295966{col 48}{space 1}  -10.60{col 57}{space 3}0.000{col 65}{space 4}-53.98027{col 78}{space 3}-37.12735
{txt}{space 18}1997  {c |}{col 25}{res}{space 2}-59.55389{col 37}{space 2} 4.298713{col 48}{space 1}  -13.85{col 57}{space 3}0.000{col 65}{space 4}-67.98574{col 78}{space 3}-51.12204
{txt}{space 18}1998  {c |}{col 25}{res}{space 2}-53.06052{col 37}{space 2} 5.077972{col 48}{space 1}  -10.45{col 57}{space 3}0.000{col 65}{space 4}-63.02087{col 78}{space 3}-43.10017
{txt}{space 18}1999  {c |}{col 25}{res}{space 2}-67.95295{col 37}{space 2} 4.083708{col 48}{space 1}  -16.64{col 57}{space 3}0.000{col 65}{space 4}-75.96307{col 78}{space 3}-59.94283
{txt}{space 18}2000  {c |}{col 25}{res}{space 2}-46.77497{col 37}{space 2}  4.79583{col 48}{space 1}   -9.75{col 57}{space 3}0.000{col 65}{space 4}-56.18191{col 78}{space 3}-37.36804
{txt}{space 18}2001  {c |}{col 25}{res}{space 2}-38.10637{col 37}{space 2} 4.962401{col 48}{space 1}   -7.68{col 57}{space 3}0.000{col 65}{space 4}-47.84003{col 78}{space 3}-28.37271
{txt}{space 18}2002  {c |}{col 25}{res}{space 2}-29.93649{col 37}{space 2} 4.031238{col 48}{space 1}   -7.43{col 57}{space 3}0.000{col 65}{space 4}-37.84369{col 78}{space 3}-22.02929
{txt}{space 18}2003  {c |}{col 25}{res}{space 2} -28.4836{col 37}{space 2} 4.236855{col 48}{space 1}   -6.72{col 57}{space 3}0.000{col 65}{space 4}-36.79412{col 78}{space 3}-20.17309
{txt}{space 18}2004  {c |}{col 25}{res}{space 2}-50.81222{col 37}{space 2} 4.092662{col 48}{space 1}  -12.42{col 57}{space 3}0.000{col 65}{space 4} -58.8399{col 78}{space 3}-42.78453
{txt}{space 18}2005  {c |}{col 25}{res}{space 2}-62.99069{col 37}{space 2} 4.512344{col 48}{space 1}  -13.96{col 57}{space 3}0.000{col 65}{space 4}-71.84157{col 78}{space 3}-54.13981
{txt}{space 18}2006  {c |}{col 25}{res}{space 2}-69.64316{col 37}{space 2} 4.781028{col 48}{space 1}  -14.57{col 57}{space 3}0.000{col 65}{space 4}-79.02106{col 78}{space 3}-60.26526
{txt}{space 18}2007  {c |}{col 25}{res}{space 2}-42.71392{col 37}{space 2} 5.854618{col 48}{space 1}   -7.30{col 57}{space 3}0.000{col 65}{space 4}-54.19765{col 78}{space 3}-31.23019
{txt}{space 18}2008  {c |}{col 25}{res}{space 2}-45.92387{col 37}{space 2} 4.576808{col 48}{space 1}  -10.03{col 57}{space 3}0.000{col 65}{space 4} -54.9012{col 78}{space 3}-36.94655
{txt}{space 18}2009  {c |}{col 25}{res}{space 2}-75.14399{col 37}{space 2}  4.65333{col 48}{space 1}  -16.15{col 57}{space 3}0.000{col 65}{space 4}-84.27141{col 78}{space 3}-66.01657
{txt}{space 18}2010  {c |}{col 25}{res}{space 2}-45.60463{col 37}{space 2} 5.096926{col 48}{space 1}   -8.95{col 57}{space 3}0.000{col 65}{space 4}-55.60216{col 78}{space 3} -35.6071
{txt}{space 18}2011  {c |}{col 25}{res}{space 2}-26.21447{col 37}{space 2} 4.765506{col 48}{space 1}   -5.50{col 57}{space 3}0.000{col 65}{space 4}-35.56193{col 78}{space 3}-16.86702
{txt}{space 18}2012  {c |}{col 25}{res}{space 2}-37.09234{col 37}{space 2} 5.695655{col 48}{space 1}   -6.51{col 57}{space 3}0.000{col 65}{space 4}-48.26427{col 78}{space 3}-25.92042
{txt}{space 18}2013  {c |}{col 25}{res}{space 2}-28.60532{col 37}{space 2} 4.911123{col 48}{space 1}   -5.82{col 57}{space 3}0.000{col 65}{space 4}-38.23839{col 78}{space 3}-18.97224
{txt}{space 18}2014  {c |}{col 25}{res}{space 2}-43.79014{col 37}{space 2} 4.840357{col 48}{space 1}   -9.05{col 57}{space 3}0.000{col 65}{space 4}-53.28441{col 78}{space 3}-34.29587
{txt}{space 18}2015  {c |}{col 25}{res}{space 2}  -69.274{col 37}{space 2} 4.526324{col 48}{space 1}  -15.30{col 57}{space 3}0.000{col 65}{space 4} -78.1523{col 78}{space 3}-60.39569
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2}  154.799{col 37}{space 2} 46.09944{col 48}{space 1}    3.36{col 57}{space 3}0.001{col 65}{space 4} 64.37573{col 78}{space 3} 245.2222
{txt}{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                sigma_u {c |} {res} 130.80057
                {txt}sigma_e {c |} {res} 75.491503
                    {txt}rho {c |} {res} .75013031{txt}   (fraction of variance due to u_i)
{hline 24}{c BT}{hline 64}
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}acres_irr {c |}{res}     35,316    167.6679    109.1458          1        900
{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved
{res}
{txt}Fixed-effects (within) regression{col 49}Number of obs{col 67}={col 69}{res}    33,953
{txt}Group variable: {res}WR_GROUP{txt}{col 49}Number of groups{col 67}={col 69}{res}     1,565

{txt}R-squared:{col 49}Obs per group:
     Within  = {res}0.0070{col 63}{txt}min{col 67}={col 69}{res}         1
{txt}     Between = {res}0.0633{col 63}{txt}avg{col 67}={col 69}{res}      21.7
{txt}     Overall = {res}0.0199{col 63}{txt}max{col 67}={col 69}{res}        25

{txt}{col 49}F({res}29{txt},{res}1564{txt}){col 67}={col 70}{res}     4.28
{txt}corr(u_i, Xb) = {res}0.1139{txt}{col 49}Prob > F{col 67}={col 73}{res}0.0000

{txt}{ralign 89:(Std. err. adjusted for {res:1,565} clusters in {res:WR_GROUP})}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}              acres_irr{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}treat_status {c |}{col 25}{res}{space 2} 6.572814{col 37}{space 2} 1.778315{col 48}{space 1}    3.70{col 57}{space 3}0.000{col 65}{space 4} 3.084682{col 78}{space 3} 10.06095
{txt}{space 5}jan_april_mean_ppt {c |}{col 25}{res}{space 2}-.0684166{col 37}{space 2} .0326032{col 48}{space 1}   -2.10{col 57}{space 3}0.036{col 65}{space 4} -.132367{col 78}{space 3}-.0044661
{txt}{space 7}may_sep_mean_ppt {c |}{col 25}{res}{space 2}-.0041958{col 37}{space 2} .0181725{col 48}{space 1}   -0.23{col 57}{space 3}0.817{col 65}{space 4}-.0398408{col 78}{space 3} .0314493
{txt}jan_april_mean_ET0_elev {c |}{col 25}{res}{space 2}  5.62653{col 37}{space 2} 3.776886{col 48}{space 1}    1.49{col 57}{space 3}0.136{col 65}{space 4}-1.781763{col 78}{space 3} 13.03482
{txt}{space 2}may_sep_mean_ET0_elev {c |}{col 25}{res}{space 2}-5.482419{col 37}{space 2} 3.623869{col 48}{space 1}   -1.51{col 57}{space 3}0.131{col 65}{space 4}-12.59057{col 78}{space 3} 1.625735
{txt}{space 23} {c |}
{space 15}wua_year {c |}
{space 18}1992  {c |}{col 25}{res}{space 2}-9.572757{col 37}{space 2} 1.945008{col 48}{space 1}   -4.92{col 57}{space 3}0.000{col 65}{space 4}-13.38785{col 78}{space 3}-5.757659
{txt}{space 18}1993  {c |}{col 25}{res}{space 2}-5.197361{col 37}{space 2} 3.803007{col 48}{space 1}   -1.37{col 57}{space 3}0.172{col 65}{space 4}-12.65689{col 78}{space 3} 2.262168
{txt}{space 18}1994  {c |}{col 25}{res}{space 2}-3.816242{col 37}{space 2} 1.812715{col 48}{space 1}   -2.11{col 57}{space 3}0.035{col 65}{space 4}-7.371849{col 78}{space 3}-.2606341
{txt}{space 18}1995  {c |}{col 25}{res}{space 2}-8.075891{col 37}{space 2} 2.279542{col 48}{space 1}   -3.54{col 57}{space 3}0.000{col 65}{space 4}-12.54717{col 78}{space 3}-3.604612
{txt}{space 18}1996  {c |}{col 25}{res}{space 2}-10.87873{col 37}{space 2} 2.235147{col 48}{space 1}   -4.87{col 57}{space 3}0.000{col 65}{space 4}-15.26293{col 78}{space 3}-6.494529
{txt}{space 18}1997  {c |}{col 25}{res}{space 2}-8.762744{col 37}{space 2} 2.482897{col 48}{space 1}   -3.53{col 57}{space 3}0.000{col 65}{space 4} -13.6329{col 78}{space 3}-3.892586
{txt}{space 18}1998  {c |}{col 25}{res}{space 2}-3.764299{col 37}{space 2} 3.238949{col 48}{space 1}   -1.16{col 57}{space 3}0.245{col 65}{space 4}-10.11744{col 78}{space 3} 2.588842
{txt}{space 18}1999  {c |}{col 25}{res}{space 2}-8.463796{col 37}{space 2}  2.30902{col 48}{space 1}   -3.67{col 57}{space 3}0.000{col 65}{space 4} -12.9929{col 78}{space 3}-3.934694
{txt}{space 18}2000  {c |}{col 25}{res}{space 2}-4.587218{col 37}{space 2} 2.652453{col 48}{space 1}   -1.73{col 57}{space 3}0.084{col 65}{space 4}-9.789956{col 78}{space 3} .6155204
{txt}{space 18}2001  {c |}{col 25}{res}{space 2}-6.550201{col 37}{space 2} 3.005299{col 48}{space 1}   -2.18{col 57}{space 3}0.029{col 65}{space 4}-12.44504{col 78}{space 3}-.6553605
{txt}{space 18}2002  {c |}{col 25}{res}{space 2}-8.451952{col 37}{space 2} 2.204121{col 48}{space 1}   -3.83{col 57}{space 3}0.000{col 65}{space 4} -12.7753{col 78}{space 3}-4.128609
{txt}{space 18}2003  {c |}{col 25}{res}{space 2}-10.46064{col 37}{space 2}  2.50821{col 48}{space 1}   -4.17{col 57}{space 3}0.000{col 65}{space 4}-15.38044{col 78}{space 3}-5.540828
{txt}{space 18}2004  {c |}{col 25}{res}{space 2}-12.21129{col 37}{space 2} 2.358859{col 48}{space 1}   -5.18{col 57}{space 3}0.000{col 65}{space 4}-16.83815{col 78}{space 3}-7.584432
{txt}{space 18}2005  {c |}{col 25}{res}{space 2}-11.58326{col 37}{space 2} 2.608012{col 48}{space 1}   -4.44{col 57}{space 3}0.000{col 65}{space 4}-16.69883{col 78}{space 3}-6.467693
{txt}{space 18}2006  {c |}{col 25}{res}{space 2}-15.99617{col 37}{space 2} 2.501027{col 48}{space 1}   -6.40{col 57}{space 3}0.000{col 65}{space 4}-20.90189{col 78}{space 3}-11.09045
{txt}{space 18}2007  {c |}{col 25}{res}{space 2}-11.32091{col 37}{space 2} 3.203154{col 48}{space 1}   -3.53{col 57}{space 3}0.000{col 65}{space 4}-17.60384{col 78}{space 3} -5.03798
{txt}{space 18}2008  {c |}{col 25}{res}{space 2}-14.63299{col 37}{space 2} 2.565518{col 48}{space 1}   -5.70{col 57}{space 3}0.000{col 65}{space 4}-19.66521{col 78}{space 3}-9.600774
{txt}{space 18}2009  {c |}{col 25}{res}{space 2}-18.26179{col 37}{space 2} 2.584819{col 48}{space 1}   -7.07{col 57}{space 3}0.000{col 65}{space 4}-23.33186{col 78}{space 3}-13.19171
{txt}{space 18}2010  {c |}{col 25}{res}{space 2}-14.34406{col 37}{space 2} 2.858891{col 48}{space 1}   -5.02{col 57}{space 3}0.000{col 65}{space 4}-19.95173{col 78}{space 3}  -8.7364
{txt}{space 18}2011  {c |}{col 25}{res}{space 2}-12.78166{col 37}{space 2} 2.659631{col 48}{space 1}   -4.81{col 57}{space 3}0.000{col 65}{space 4}-17.99847{col 78}{space 3}-7.564838
{txt}{space 18}2012  {c |}{col 25}{res}{space 2}-11.24825{col 37}{space 2} 2.961931{col 48}{space 1}   -3.80{col 57}{space 3}0.000{col 65}{space 4}-17.05802{col 78}{space 3}-5.438476
{txt}{space 18}2013  {c |}{col 25}{res}{space 2}-12.09483{col 37}{space 2} 2.713522{col 48}{space 1}   -4.46{col 57}{space 3}0.000{col 65}{space 4}-17.41735{col 78}{space 3}-6.772301
{txt}{space 18}2014  {c |}{col 25}{res}{space 2}-15.43349{col 37}{space 2} 2.629067{col 48}{space 1}   -5.87{col 57}{space 3}0.000{col 65}{space 4}-20.59035{col 78}{space 3}-10.27662
{txt}{space 18}2015  {c |}{col 25}{res}{space 2}-17.55666{col 37}{space 2} 2.462659{col 48}{space 1}   -7.13{col 57}{space 3}0.000{col 65}{space 4}-22.38712{col 78}{space 3} -12.7262
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} 186.7978{col 37}{space 2} 22.66779{col 48}{space 1}    8.24{col 57}{space 3}0.000{col 65}{space 4} 142.3353{col 78}{space 3} 231.2603
{txt}{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                sigma_u {c |} {res} 104.87686
                {txt}sigma_e {c |} {res} 41.546871
                    {txt}rho {c |} {res} .86435347{txt}   (fraction of variance due to u_i)
{hline 24}{c BT}{hline 64}
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
depth_appl~d {c |}{res}     35,316    1.154986    .5173636   .0002222    2.47476
{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved
{res}
{txt}Fixed-effects (within) regression{col 49}Number of obs{col 67}={col 69}{res}    33,953
{txt}Group variable: {res}WR_GROUP{txt}{col 49}Number of groups{col 67}={col 69}{res}     1,565

{txt}R-squared:{col 49}Obs per group:
     Within  = {res}0.1543{col 63}{txt}min{col 67}={col 69}{res}         1
{txt}     Between = {res}0.1093{col 63}{txt}avg{col 67}={col 69}{res}      21.7
{txt}     Overall = {res}0.1412{col 63}{txt}max{col 67}={col 69}{res}        25

{txt}{col 49}F({res}29{txt},{res}1564{txt}){col 67}={col 70}{res}   164.94
{txt}corr(u_i, Xb) = {res}0.0083{txt}{col 49}Prob > F{col 67}={col 73}{res}0.0000

{txt}{ralign 89:(Std. err. adjusted for {res:1,565} clusters in {res:WR_GROUP})}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}          depth_applied{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}treat_status {c |}{col 25}{res}{space 2}-.1446795{col 37}{space 2} .0126097{col 48}{space 1}  -11.47{col 57}{space 3}0.000{col 65}{space 4}-.1694133{col 78}{space 3}-.1199458
{txt}{space 5}jan_april_mean_ppt {c |}{col 25}{res}{space 2}-.0021359{col 37}{space 2} .0003431{col 48}{space 1}   -6.23{col 57}{space 3}0.000{col 65}{space 4}-.0028088{col 78}{space 3} -.001463
{txt}{space 7}may_sep_mean_ppt {c |}{col 25}{res}{space 2}-.0043425{col 37}{space 2}  .000186{col 48}{space 1}  -23.35{col 57}{space 3}0.000{col 65}{space 4}-.0047073{col 78}{space 3}-.0039777
{txt}jan_april_mean_ET0_elev {c |}{col 25}{res}{space 2} .2051566{col 37}{space 2} .0354142{col 48}{space 1}    5.79{col 57}{space 3}0.000{col 65}{space 4} .1356923{col 78}{space 3} .2746209
{txt}{space 2}may_sep_mean_ET0_elev {c |}{col 25}{res}{space 2} .1170101{col 37}{space 2} .0342896{col 48}{space 1}    3.41{col 57}{space 3}0.001{col 65}{space 4} .0497517{col 78}{space 3} .1842686
{txt}{space 23} {c |}
{space 15}wua_year {c |}
{space 18}1992  {c |}{col 25}{res}{space 2}  -.28014{col 37}{space 2} .0195955{col 48}{space 1}  -14.30{col 57}{space 3}0.000{col 65}{space 4}-.3185761{col 78}{space 3}-.2417038
{txt}{space 18}1993  {c |}{col 25}{res}{space 2}-.0911065{col 37}{space 2} .0342549{col 48}{space 1}   -2.66{col 57}{space 3}0.008{col 65}{space 4}-.1582969{col 78}{space 3}-.0239161
{txt}{space 18}1994  {c |}{col 25}{res}{space 2}-.1936577{col 37}{space 2}  .017265{col 48}{space 1}  -11.22{col 57}{space 3}0.000{col 65}{space 4}-.2275227{col 78}{space 3}-.1597927
{txt}{space 18}1995  {c |}{col 25}{res}{space 2}-.0463236{col 37}{space 2} .0202579{col 48}{space 1}   -2.29{col 57}{space 3}0.022{col 65}{space 4} -.086059{col 78}{space 3}-.0065882
{txt}{space 18}1996  {c |}{col 25}{res}{space 2}-.1249901{col 37}{space 2} .0200577{col 48}{space 1}   -6.23{col 57}{space 3}0.000{col 65}{space 4} -.164333{col 78}{space 3}-.0856472
{txt}{space 18}1997  {c |}{col 25}{res}{space 2}-.2427279{col 37}{space 2} .0203514{col 48}{space 1}  -11.93{col 57}{space 3}0.000{col 65}{space 4}-.2826467{col 78}{space 3} -.202809
{txt}{space 18}1998  {c |}{col 25}{res}{space 2}-.2565616{col 37}{space 2} .0273498{col 48}{space 1}   -9.38{col 57}{space 3}0.000{col 65}{space 4}-.3102077{col 78}{space 3}-.2029155
{txt}{space 18}1999  {c |}{col 25}{res}{space 2} -.282791{col 37}{space 2} .0195819{col 48}{space 1}  -14.44{col 57}{space 3}0.000{col 65}{space 4}-.3212004{col 78}{space 3}-.2443815
{txt}{space 18}2000  {c |}{col 25}{res}{space 2}-.2468266{col 37}{space 2}  .023824{col 48}{space 1}  -10.36{col 57}{space 3}0.000{col 65}{space 4} -.293557{col 78}{space 3}-.2000962
{txt}{space 18}2001  {c |}{col 25}{res}{space 2}-.1399778{col 37}{space 2} .0260505{col 48}{space 1}   -5.37{col 57}{space 3}0.000{col 65}{space 4}-.1910754{col 78}{space 3}-.0888802
{txt}{space 18}2002  {c |}{col 25}{res}{space 2} -.104714{col 37}{space 2} .0196954{col 48}{space 1}   -5.32{col 57}{space 3}0.000{col 65}{space 4}-.1433461{col 78}{space 3}-.0660818
{txt}{space 18}2003  {c |}{col 25}{res}{space 2}-.0509108{col 37}{space 2} .0210131{col 48}{space 1}   -2.42{col 57}{space 3}0.016{col 65}{space 4}-.0921276{col 78}{space 3} -.009694
{txt}{space 18}2004  {c |}{col 25}{res}{space 2}-.1602773{col 37}{space 2}   .01961{col 48}{space 1}   -8.17{col 57}{space 3}0.000{col 65}{space 4} -.198742{col 78}{space 3}-.1218126
{txt}{space 18}2005  {c |}{col 25}{res}{space 2}-.2344175{col 37}{space 2} .0218665{col 48}{space 1}  -10.72{col 57}{space 3}0.000{col 65}{space 4}-.2773083{col 78}{space 3}-.1915267
{txt}{space 18}2006  {c |}{col 25}{res}{space 2} -.259708{col 37}{space 2} .0224891{col 48}{space 1}  -11.55{col 57}{space 3}0.000{col 65}{space 4}  -.30382{col 78}{space 3} -.215596
{txt}{space 18}2007  {c |}{col 25}{res}{space 2}-.1519787{col 37}{space 2} .0291536{col 48}{space 1}   -5.21{col 57}{space 3}0.000{col 65}{space 4}-.2091629{col 78}{space 3}-.0947944
{txt}{space 18}2008  {c |}{col 25}{res}{space 2}-.1588465{col 37}{space 2} .0216985{col 48}{space 1}   -7.32{col 57}{space 3}0.000{col 65}{space 4}-.2014076{col 78}{space 3}-.1162853
{txt}{space 18}2009  {c |}{col 25}{res}{space 2}-.2813786{col 37}{space 2} .0225073{col 48}{space 1}  -12.50{col 57}{space 3}0.000{col 65}{space 4}-.3255263{col 78}{space 3} -.237231
{txt}{space 18}2010  {c |}{col 25}{res}{space 2}-.1580308{col 37}{space 2} .0254594{col 48}{space 1}   -6.21{col 57}{space 3}0.000{col 65}{space 4} -.207969{col 78}{space 3}-.1080926
{txt}{space 18}2011  {c |}{col 25}{res}{space 2}-.0756852{col 37}{space 2} .0248025{col 48}{space 1}   -3.05{col 57}{space 3}0.002{col 65}{space 4}-.1243349{col 78}{space 3}-.0270355
{txt}{space 18}2012  {c |}{col 25}{res}{space 2}-.1435732{col 37}{space 2} .0280267{col 48}{space 1}   -5.12{col 57}{space 3}0.000{col 65}{space 4}-.1985471{col 78}{space 3}-.0885994
{txt}{space 18}2013  {c |}{col 25}{res}{space 2}-.0576343{col 37}{space 2} .0244936{col 48}{space 1}   -2.35{col 57}{space 3}0.019{col 65}{space 4} -.105678{col 78}{space 3}-.0095907
{txt}{space 18}2014  {c |}{col 25}{res}{space 2}-.1172589{col 37}{space 2} .0229725{col 48}{space 1}   -5.10{col 57}{space 3}0.000{col 65}{space 4}-.1623191{col 78}{space 3}-.0721987
{txt}{space 18}2015  {c |}{col 25}{res}{space 2}-.2116547{col 37}{space 2} .0207201{col 48}{space 1}  -10.21{col 57}{space 3}0.000{col 65}{space 4}-.2522967{col 78}{space 3}-.1710126
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .5882354{col 37}{space 2} .2020413{col 48}{space 1}    2.91{col 57}{space 3}0.004{col 65}{space 4} .1919351{col 78}{space 3} .9845357
{txt}{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                sigma_u {c |} {res} .30420314
                {txt}sigma_e {c |} {res} .38640307
                    {txt}rho {c |} {res} .38263694{txt}   (fraction of variance due to u_i)
{hline 24}{c BT}{hline 64}
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

{com}. 
. *Now do event study
.         // Create variable showing time relative to treatment for eventdd
.         gen t_to = wua_year - cohort_flood_cplepa  
{txt}
{com}.         replace t_to = . if cohort_flood_cplepa==0
{txt}(0 real changes made)

{com}.         putexcel set "$dr_temp\twfe_event_study.xlsx", ///
>                 sheet("floodtocplepa_91_15_nyt") modify
{res}{txt}
{com}.         putexcel A1 = "stata_name"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe_event_study.xlsx} saved

{com}.         putexcel B1 = "ci_low_95"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe_event_study.xlsx} saved

{com}.         putexcel C1 = "estimate"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe_event_study.xlsx} saved

{com}.         putexcel D1 = "ci_high_95"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe_event_study.xlsx} saved

{com}.         putexcel E1 = "effect_estimated"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe_event_study.xlsx} saved

{com}.         putexcel F1 = "dep_var"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe_event_study.xlsx} saved

{com}.         putexcel G1 = "panel_mean_dep_var"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe_event_study.xlsx} saved

{com}.         local i = 0
{txt}
{com}.         foreach y in "af_used"  "acres_irr" "depth_applied" {c -(}
{txt}  2{com}.                 local starter = 2 + `i'*48
{txt}  3{com}.                 local ender = `starter' + 24
{txt}  4{com}.                 local end_vec = `starter' + 47
{txt}  5{com}.                 *Label the periods being estimated (relative to treatment)
.                         *Pre-treatment
.                         local pre_start = `starter'
{txt}  6{com}.                         local pre_stop = `ender' - 1
{txt}  7{com}.                         local cur_eff = -1
{txt}  8{com}.                         forvalues eff_cell=`pre_start'/`pre_stop' {c -(}
{txt}  9{com}.                                 putexcel E`eff_cell' = `cur_eff'
{txt} 10{com}.                                 local cur_eff = `cur_eff' - 1
{txt} 11{com}.                         {c )-}
{txt} 12{com}.                         *Post-treatment
.                         local post_start = `ender'
{txt} 13{com}.                         local post_stop = `end_vec'
{txt} 14{com}.                         local cur_eff = 0
{txt} 15{com}.                         forvalues eff_cell=`post_start'/`post_stop' {c -(}
{txt} 16{com}.                                 putexcel E`eff_cell' = `cur_eff'
{txt} 17{com}.                                 local cur_eff = `cur_eff' + 1
{txt} 18{com}.                         {c )-}
{txt} 19{com}.                 *Label dependent variable
.                 putexcel F`starter':F`end_vec' = "`y'"
{txt} 20{com}.                 *Add sub-sample mean of dep var
.                 sum `y'
{txt} 21{com}.                 local var_mean = r(mean)
{txt} 22{com}.                         putexcel G`starter':G`end_vec' = `var_mean'
{txt} 23{com}.                 *perform event study and save results
.                         qui eventdd `y' jan_april_mean_ppt may_sep_mean_ppt ///
>                                 jan_april_mean_ET0_elev may_sep_mean_ET0_elev i.wua_year, ///
>                                 timevar(t_to) method(fe, cluster(WR_GROUP)) graph_op(leg(off))          
{txt} 24{com}.                 putexcel A`starter' = matrix(e(leads))
{txt} 25{com}.                 putexcel A`ender' = matrix(e(lags))
{txt} 26{com}.                 local ++i
{txt} 27{com}.         {c )-}
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe_event_study.xlsx} saved
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe_event_study.xlsx} saved
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe_event_study.xlsx} saved
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe_event_study.xlsx} saved
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe_event_study.xlsx} saved
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe_event_study.xlsx} saved
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe_event_study.xlsx} saved
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe_event_study.xlsx} saved
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe_event_study.xlsx} saved
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    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}af_used {c |}{res}     35,316    192.0391    151.6318        .01    1404.87
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    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}acres_irr {c |}{res}     35,316    167.6679    109.1458          1        900
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    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
depth_appl~d {c |}{res}     35,316    1.154986    .5173636   .0002222    2.47476
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{com}. 
. ********************************************************************************
. ***************** Center Pivot to LEPA *****************************************
. ********************************************************************************
. use "$dr_temp\wrg_collapsed.dta", replace
{txt}
{com}.         *Check how many WR_GROUPS are missing aquifer characteristics
.         xtset WR_GROUP wua_year
{res}
{col 1}{txt:Panel variable: }{res:WR_GROUP}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:wua_year}{txt:, }{res:{bind:1991}}{txt: to }{res:{bind:2019}}{txt:, but with gaps}{p_end}
{txt}{col 10}Delta: {res}1 unit
{txt}
{com}.         xtdescribe  /* There are 18,541 WR_GROUP's in this dataset only using wuadet_key records with IRR umw_code */

{txt}WR_GROUP:  {res}1{txt}, {res}3{txt}, ..., {res}21983                                  {txt}n ={res}      18541
{txt}wua_year:  {res}1991, 1992, ..., 2019                             {txt}T ={res}         29
           {txt}Delta(wua_year) = {res}1 unit
           {txt}Span(wua_year)  = {res}29 periods
           {txt}(WR_GROUP*wua_year uniquely identifies each observation)

Distribution of T_i:   min      5%     25%       50%       75%     95%     max
                    {res}     1       5      22        29        29      29      29

{txt}{col 6}Freq.  Percent    Cum. {c |}  Pattern
 {hline 27}{c +}{c -}{c -}{hline 29}
{res}     9985     53.85   53.85{txt} {c |}  {res}11111111111111111111111111111
      778      4.20   58.05{txt} {c |}  {res}11111........................
      197      1.06   59.11{txt} {c |}  {res}11111.11111111111111111111111
      176      0.95   60.06{txt} {c |}  {res}1111111111111111111111111111.
      132      0.71   60.77{txt} {c |}  {res}111111.......................
      129      0.70   61.47{txt} {c |}  {res}1............................
      121      0.65   62.12{txt} {c |}  {res}1111111.111111111111111111111
      118      0.64   62.76{txt} {c |}  {res}111111111.1111111111111111111
      100      0.54   63.30{txt} {c |}  {res}1111111111.111111111111111111
     6805     36.70  100.00{txt} {c |} (other patterns)
 {hline 27}{c +}{c -}{c -}{hline 29}
{res}    18541    100.00        {txt} {c |}  {res}XXXXXXXXXXXXXXXXXXXXXXXXXXXXX
{txt}
{com}. 
. /*Create a irrigation intensity outcome variable */
.         bysort WR_GROUP wua_year: gen depth_applied = af_used_irr/acres_irr
{txt}(12,834 missing values generated)

{com}.         
. /* Address missing and extreme values */
.         *Drop wr_groups who don't have predevelopment characteristics
.         keep if !missing(sy)
{txt}(70,993 observations deleted)

{com}.         keep if !missing(predev_sat)
{txt}(0 observations deleted)

{com}.         keep if !missing(predev_dtw)
{txt}(0 observations deleted)

{com}.         keep if !missing(hyd_cond)
{txt}(0 observations deleted)

{com}.         *Drop wr_groups if they have missing precip or soil data
.         drop if missing(jan_april_mean_ppt)
{txt}(0 observations deleted)

{com}.         drop if missing(awc)
{txt}(885 observations deleted)

{com}.         *Drop wr_groups if they have missing authorized quantities or acreage
.         drop if missing(wrg_authquant_IRR_umw) | missing(wrg_authquant_all_umw) | missing(wrg_auth_irr_acres)
{txt}(8,674 observations deleted)

{com}.         *Drop observations with recorded withdrawals but no irrigated acres and vice-versa
.         drop if af_used_irr>0 & acres_irr==0
{txt}(495 observations deleted)

{com}.         drop if acres_irr>0 & af_used_irr==0
{txt}(0 observations deleted)

{com}.         *Drop extreme values for af_used, acres_irr 
.                 sum af_used, d

                        {txt}(sum) af_used
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}       19              0
{txt}10%    {res}       44              0       {txt}Obs         {res}    363,631
{txt}25%    {res}       96              0       {txt}Sum of wgt. {res}    363,631

{txt}50%    {res}      161                      {txt}Mean          {res} 243.0458
                        {txt}Largest       Std. dev.     {res}  362.545
{txt}75%    {res}      264       15725.45
{txt}90%    {res}   500.84       16005.28       {txt}Variance      {res} 131438.9
{txt}95%    {res}      729       16191.03       {txt}Skewness      {res} 13.12807
{txt}99%    {res}  1431.82        18592.3       {txt}Kurtosis      {res} 365.9421
{txt}
{com}.                         drop if af_used >= r(p99) & af_used < .
{txt}(3,637 observations deleted)

{com}.                 sum acres_irr, d

                       {txt}(sum) acres_irr
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}       40              0
{txt}10%    {res}       73              0       {txt}Obs         {res}    359,994
{txt}25%    {res}      120              0       {txt}Sum of wgt. {res}    359,994

{txt}50%    {res}      130                      {txt}Mean          {res} 197.6613
                        {txt}Largest       Std. dev.     {res}   176.09
{txt}75%    {res}      240           3540
{txt}90%    {res}      390           3780       {txt}Variance      {res}  31007.7
{txt}95%    {res}      523           4000       {txt}Skewness      {res} 3.452098
{txt}99%    {res}      911           5120       {txt}Kurtosis      {res} 25.24593
{txt}
{com}.                         drop if acres_irr >= r(p99) & acres_irr < .
{txt}(3,605 observations deleted)

{com}.                 sum depth_applied, d

                        {txt}depth_applied
{hline 61}
      Percentiles      Smallest
 1%    {res}       .1        .000069
{txt} 5%    {res} .3129231       .0000769
{txt}10%    {res}     .472         .00008       {txt}Obs         {res}    350,366
{txt}25%    {res} .7586667       .0000833       {txt}Sum of wgt. {res}    350,366

{txt}50%    {res} 1.097462                      {txt}Mean          {res} 1.123839
                        {txt}Largest       Std. dev.     {res} .5311018
{txt}75%    {res} 1.449077          16.57
{txt}90%    {res} 1.799385         17.415       {txt}Variance      {res} .2820692
{txt}95%    {res}        2          27.87       {txt}Skewness      {res} 1.816609
{txt}99%    {res}    2.475          29.83       {txt}Kurtosis      {res} 57.22109
{txt}
{com}.                         drop if depth_applied >= r(p99) & depth_applied < .             
{txt}(3,513 observations deleted)

{com}.         *Change total precipitation and evapotranspiration variables to inches
.         foreach var of varlist total_preseason_ET0_elev total_growseason_ET0_elev ///
>                 total_preseason_ET0_Har total_growseason_ET0_Har total_preseason_ppt ///
>                 total_growseason_ppt {c -(}
{txt}  2{com}.                         replace `var' = `var'/25.4
{txt}  3{com}.                 {c )-}
{txt}(335,118 real changes made)
(335,118 real changes made)
(352,876 real changes made)
(352,876 real changes made)
(352,876 real changes made)
(352,876 real changes made)

{com}.         
. /* Label variables */
.         label var wua_year "Year"
{txt}
{com}.         label var af_used "Acre-feet applied"
{txt}
{com}.         label var acres_irr "Irrigated acres"
{txt}
{com}.         label var depth_applied "Depth applied - acre-feet applied per acre"
{txt}
{com}.         label var awc "Available water capacity"
{txt}
{com}.         label var sandtotal "Sand content (%)"
{txt}
{com}.         label var silttotal "Silt content (%)"
{txt}
{com}.         label var slope "Slope"
{txt}
{com}.         label var jan_april_mean_ppt "mean monthly preseason precipitation (mm)"
{txt}
{com}.         label var may_sep_mean_ppt "mean monthly growing season precipitation (mm)"
{txt}
{com}.         label var jan_april_mean_ET0_elev "mean daily preseason evapotranspiration, using elevation from ssurgo"
{txt}
{com}.         label var may_sep_mean_ET0_elev "mean daily growing season evapotranspiration, using elevation from ssurgo"
{txt}
{com}.         label var jan_april_mean_ET0_Har "mean daily preseason evapotranspiration"
{txt}
{com}.         label var may_sep_mean_ET0_Har "mean daily growing season evapotranspiration"
{txt}
{com}. 
. *Remove wr_groups with other technologies or with fractional data
. *for systems codes we're interested in *
.         drop if type_system_2 > 0 | type_system_5 > 0 | type_system_6 > 0 ///
>         | type_system_7 > 0 | type_system_8 > 0 | type_system_9 > 0
{txt}(37,437 observations deleted)

{com}.         *Drop fractional system_codes
.                 foreach var of varlist type_system_1 type_system_3 type_system_4 {c -(}
{txt}  2{com}.                         keep if `var'==1 | `var'==0
{txt}  3{com}.                 {c )-}
{txt}(5,491 observations deleted)
(1,919 observations deleted)
(0 observations deleted)

{com}. 
. *Check how many groups switch into each final system type multiple times
.         gen switch_flood_cp = l.type_system_1==1 & type_system_3==1
{txt}
{com}.                 by WR_GROUP: egen num_switch_flood_cp = total(switch_flood_cp)
{txt}
{com}.                 tab num_switch_flood_cp

{txt}num_switch_ {c |}
   flood_cp {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}    280,921       91.20       91.20
{txt}          1 {c |}{res}     25,992        8.44       99.64
{txt}          2 {c |}{res}      1,104        0.36      100.00
{txt}          3 {c |}{res}         12        0.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}    308,029      100.00
{txt}
{com}.         gen switch_flood_lepa = l.type_system_1==1 & type_system_4==1
{txt}
{com}.                 by WR_GROUP: egen num_switch_flood_lepa = total(switch_flood_lepa)
{txt}
{com}.                 tab num_switch_flood_lepa

{txt}num_switch_ {c |}
 flood_lepa {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}    276,352       89.72       89.72
{txt}          1 {c |}{res}     30,195        9.80       99.52
{txt}          2 {c |}{res}      1,432        0.46       99.98
{txt}          3 {c |}{res}         50        0.02      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}    308,029      100.00
{txt}
{com}.         gen switch_cp_lepa = l.type_system_3==1 & type_system_4==1
{txt}
{com}.                 by WR_GROUP: egen num_switch_cp_lepa = total(switch_cp_lepa)
{txt}
{com}.                 tab num_switch_cp_lepa

{txt}num_switch_ {c |}
    cp_lepa {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}     93,358       30.31       30.31
{txt}          1 {c |}{res}    131,349       42.64       72.95
{txt}          2 {c |}{res}     53,754       17.45       90.40
{txt}          3 {c |}{res}     19,214        6.24       96.64
{txt}          4 {c |}{res}      8,074        2.62       99.26
{txt}          5 {c |}{res}      1,584        0.51       99.77
{txt}          6 {c |}{res}        584        0.19       99.96
{txt}          7 {c |}{res}        112        0.04      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}    308,029      100.00
{txt}
{com}.         gen switch_flood_cplepa = l.type_system_1==1 & type_system_3==1
{txt}
{com}.                 /* Also include those who switch from flood straight to LEPA */
.                 replace switch_flood_cplepa = 1 if l.type_system_1==1 & type_system_4==1
{txt}(1,324 real changes made)

{com}.                 by WR_GROUP: egen num_switch_flood_cplepa = total(switch_flood_cplepa)
{txt}
{com}.                 tab num_switch_flood_cplepa

{txt}num_switch_ {c |}
flood_cplep {c |}
          a {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}    251,224       81.56       81.56
{txt}          1 {c |}{res}     52,439       17.02       98.58
{txt}          2 {c |}{res}      4,147        1.35       99.93
{txt}          3 {c |}{res}        164        0.05       99.98
{txt}          4 {c |}{res}         55        0.02      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}    308,029      100.00
{txt}
{com}. 
. *Create variables showing reversion from one to another, and how many times they revert
.         gen reversion_cp_flood = l.type_system_3==1 & type_system_1==1
{txt}
{com}.                 by WR_GROUP: egen num_revers_cp_flood = total(reversion_cp_flood)
{txt}
{com}.                 tab num_revers_cp_flood

{txt}num_revers_ {c |}
   cp_flood {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}    299,089       97.10       97.10
{txt}          1 {c |}{res}      8,619        2.80       99.90
{txt}          2 {c |}{res}        321        0.10      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}    308,029      100.00
{txt}
{com}.         gen reversion_lepa_flood = l.type_system_4==1 & type_system_1==1
{txt}
{com}.                 by WR_GROUP: egen num_revers_lepa_flood = total(reversion_lepa_flood)
{txt}
{com}.                 tab num_revers_lepa_flood

{txt}num_revers_ {c |}
 lepa_flood {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}    300,036       97.41       97.41
{txt}          1 {c |}{res}      7,711        2.50       99.91
{txt}          2 {c |}{res}        282        0.09      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}    308,029      100.00
{txt}
{com}.         gen reversion_lepa_cp = l.type_system_4==1 & type_system_3==1
{txt}
{com}.                 by WR_GROUP: egen num_revers_lepa_cp = total(reversion_lepa_cp)
{txt}
{com}.                 tab num_revers_lepa_cp

{txt}num_revers_ {c |}
    lepa_cp {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}    193,997       62.98       62.98
{txt}          1 {c |}{res}     73,024       23.71       86.69
{txt}          2 {c |}{res}     27,200        8.83       95.52
{txt}          3 {c |}{res}     10,085        3.27       98.79
{txt}          4 {c |}{res}      2,884        0.94       99.73
{txt}          5 {c |}{res}        585        0.19       99.92
{txt}          6 {c |}{res}        254        0.08      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}    308,029      100.00
{txt}
{com}.         gen reversion_cplepa_flood = l.type_system_3==1 & type_system_1==1
{txt}
{com}.                 replace reversion_cplepa_flood = 1 if l.type_system_4==1 & type_system_1==1
{txt}(336 real changes made)

{com}.                 by WR_GROUP: egen num_revers_cplepa_flood = total(reversion_cplepa_flood)
{txt}
{com}.                 tab num_revers_cplepa_flood

{txt}num_revers_ {c |}
cplepa_floo {c |}
          d {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}    291,661       94.69       94.69
{txt}          1 {c |}{res}     15,310        4.97       99.66
{txt}          2 {c |}{res}        975        0.32       99.97
{txt}          3 {c |}{res}         56        0.02       99.99
{txt}          4 {c |}{res}         27        0.01      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}    308,029      100.00
{txt}
{com}. 
. *Drop multiple switchers or reverters
.         drop if num_switch_flood_cp>=2
{txt}(1,116 observations deleted)

{com}.         drop if num_revers_cp_flood>=1
{txt}(7,908 observations deleted)

{com}.         drop if num_switch_flood_lepa>=2
{txt}(1,257 observations deleted)

{com}.         drop if num_revers_lepa_flood>=1
{txt}(6,291 observations deleted)

{com}.         drop if num_switch_cp_lepa>=2
{txt}(79,318 observations deleted)

{com}.         drop if num_revers_lepa_cp>=1
{txt}(31,009 observations deleted)

{com}.         drop if num_switch_flood_cplepa>=2
{txt}(78 observations deleted)

{com}.         drop if num_revers_cplepa_flood>=1
{txt}(0 observations deleted)

{com}.                 
. *Create variable with gmd number
.         gen gmd = 0
{txt}
{com}.         forvalues i=1(1)5 {c -(}
{txt}  2{com}.                 replace gmd = `i' if gmd_`i'==1
{txt}  3{com}.         {c )-}
{txt}(12,965 real changes made)
(15,462 real changes made)
(59,540 real changes made)
(29,146 real changes made)
(52,016 real changes made)

{com}. 
. ***************** Creation of treatment and cohort variables *******************
.         *Keep only traditional center pivot and LEPA water right groups
.         keep if type_system_3==1 | type_system_4==1 
{txt}(34,476 observations deleted)

{com}.         xtdescribe // *Now we have 7405 total water right groups

{txt}WR_GROUP:  {res}4{txt}, {res}5{txt}, ..., {res}21916                                  {txt}n ={res}       7405
{txt}wua_year:  {res}1991, 1992, ..., 2019                             {txt}T ={res}         29
           {txt}Delta(wua_year) = {res}1 unit
           {txt}Span(wua_year)  = {res}29 periods
           {txt}(WR_GROUP*wua_year uniquely identifies each observation)

Distribution of T_i:   min      5%     25%       50%       75%     95%     max
                    {res}     1       2      13        23        28      29      29

{txt}{col 6}Freq.  Percent    Cum. {c |}  Pattern
 {hline 27}{c +}{c -}{c -}{hline 29}
{res}     1691     22.84   22.84{txt} {c |}  {res}11111111111111111111111111111
      102      1.38   24.21{txt} {c |}  {res}......11111111111111111111111
       98      1.32   25.54{txt} {c |}  {res}.....111111111111111111111111
       94      1.27   26.81{txt} {c |}  {res}.1111111111111111111111111111
       90      1.22   28.02{txt} {c |}  {res}1............................
       84      1.13   29.16{txt} {c |}  {res}.......1111111111111111111111
       82      1.11   30.26{txt} {c |}  {res}......................1111111
       79      1.07   31.33{txt} {c |}  {res}.....................11111111
       73      0.99   32.32{txt} {c |}  {res}.......................111111
     5012     67.68  100.00{txt} {c |} (other patterns)
 {hline 27}{c +}{c -}{c -}{hline 29}
{res}     7405    100.00        {txt} {c |}  {res}XXXXXXXXXXXXXXXXXXXXXXXXXXXXX
{txt}
{com}.         distinct(WR_GROUP) if num_switch_cp_lepa==1 /*3824 who with an observable switch from cp to lepa */

{txt}{col 11}{c |}        Observations
{col 11}{c |}      total   distinct
{hline 10}{c +}{hline 22}
 WR_GROUP {c |}  {res}    97783       3824
{txt}
{com}.         distinct(WR_GROUP) if num_switch_cp_lepa==0 /* 3,581 wr_groups who only have cp */

{txt}{col 11}{c |}        Observations
{col 11}{c |}      total   distinct
{hline 10}{c +}{hline 22}
 WR_GROUP {c |}  {res}    48793       3581
{txt}
{com}.         xtdescribe //7405 total

{txt}WR_GROUP:  {res}4{txt}, {res}5{txt}, ..., {res}21916                                  {txt}n ={res}       7405
{txt}wua_year:  {res}1991, 1992, ..., 2019                             {txt}T ={res}         29
           {txt}Delta(wua_year) = {res}1 unit
           {txt}Span(wua_year)  = {res}29 periods
           {txt}(WR_GROUP*wua_year uniquely identifies each observation)

Distribution of T_i:   min      5%     25%       50%       75%     95%     max
                    {res}     1       2      13        23        28      29      29

{txt}{col 6}Freq.  Percent    Cum. {c |}  Pattern
 {hline 27}{c +}{c -}{c -}{hline 29}
{res}     1691     22.84   22.84{txt} {c |}  {res}11111111111111111111111111111
      102      1.38   24.21{txt} {c |}  {res}......11111111111111111111111
       98      1.32   25.54{txt} {c |}  {res}.....111111111111111111111111
       94      1.27   26.81{txt} {c |}  {res}.1111111111111111111111111111
       90      1.22   28.02{txt} {c |}  {res}1............................
       84      1.13   29.16{txt} {c |}  {res}.......1111111111111111111111
       82      1.11   30.26{txt} {c |}  {res}......................1111111
       79      1.07   31.33{txt} {c |}  {res}.....................11111111
       73      0.99   32.32{txt} {c |}  {res}.......................111111
     5012     67.68  100.00{txt} {c |} (other patterns)
 {hline 27}{c +}{c -}{c -}{hline 29}
{res}     7405    100.00        {txt} {c |}  {res}XXXXXXXXXXXXXXXXXXXXXXXXXXXXX
{txt}
{com}. 
.         *Check sample stats and make sure treatment and control groups are accurate
.         sum af_used acres_irr depth_applied

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}af_used {c |}{res}    146,576    214.9692    185.8918        .01       1431
{txt}{space 3}acres_irr {c |}{res}    146,576     192.542    135.7555          1        910
{txt}depth_appl~d {c |}{res}    146,576    1.108706    .4710121   .0000769    2.47476
{txt}
{com}.         bysort WR_GROUP: egen any_cp = max(type_system_3)
{txt}
{com}.         bysort WR_GROUP: egen any_lepa = max(type_system_4)
{txt}
{com}.                 *Check to see if we have any lepa users who we don't observe the switch for
.                 tab any_lepa if num_switch_cp_lepa==0 

   {txt}any_lepa {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      2,981        6.11        6.11
{txt}          1 {c |}{res}     45,812       93.89      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     48,793      100.00
{txt}
{com}.                 *DROP THEM
.                 drop if any_lepa==1 & num_switch_cp_lepa==0 /* 45,812 observations deleted */
{txt}(45,812 observations deleted)

{com}.                 xtdescribe //4097 wrgs

{txt}WR_GROUP:  {res}4{txt}, {res}5{txt}, ..., {res}21871                                  {txt}n ={res}       4097
{txt}wua_year:  {res}1991, 1992, ..., 2019                             {txt}T ={res}         29
           {txt}Delta(wua_year) = {res}1 unit
           {txt}Span(wua_year)  = {res}29 periods
           {txt}(WR_GROUP*wua_year uniquely identifies each observation)

Distribution of T_i:   min      5%     25%       50%       75%     95%     max
                    {res}     1       6      23        28        29      29      29

{txt}{col 6}Freq.  Percent    Cum. {c |}  Pattern
 {hline 27}{c +}{c -}{c -}{hline 29}
{res}     1691     41.27   41.27{txt} {c |}  {res}11111111111111111111111111111
       80      1.95   43.23{txt} {c |}  {res}.1111111111111111111111111111
       72      1.76   44.98{txt} {c |}  {res}.....111111111111111111111111
       55      1.34   46.33{txt} {c |}  {res}....1111111111111111111111111
       45      1.10   47.42{txt} {c |}  {res}......11111111111111111111111
       42      1.03   48.45{txt} {c |}  {res}111111111111.1111111111111111
       40      0.98   49.43{txt} {c |}  {res}1.111111111111111111111111111
       40      0.98   50.40{txt} {c |}  {res}11111111111.11111111111111111
       39      0.95   51.35{txt} {c |}  {res}...11111111111111111111111111
     1993     48.65  100.00{txt} {c |} (other patterns)
 {hline 27}{c +}{c -}{c -}{hline 29}
{res}     4097    100.00        {txt} {c |}  {res}XXXXXXXXXXXXXXXXXXXXXXXXXXXXX
{txt}
{com}.         
.         *Create variable for first year of treatment to be the cohort year
.         tab switch_cp_lepa wua_year

{txt}switch_cp_ {c |}                                                                Year
      lepa {c |}      1991       1992       1993       1994       1995       1996       1997       1998       1999       2000       2001       2002 {c |}     Total
{hline 11}{c +}{hline 132}{c +}{hline 10}
         0 {c |}{res}     2,828      2,828      2,921      3,021      3,100      3,264      2,543      2,960      3,254      3,063      3,341      3,315 {txt}{c |}{res}    96,940 
{txt}         1 {c |}{res}         0         87         40         48         63         73        932        531        285        447        178        166 {txt}{c |}{res}     3,824 
{txt}{hline 11}{c +}{hline 132}{c +}{hline 10}
     Total {c |}{res}     2,828      2,915      2,961      3,069      3,163      3,337      3,475      3,491      3,539      3,510      3,519      3,481 {txt}{c |}{res}   100,764 


{txt}switch_cp_ {c |}                                                                Year
      lepa {c |}      2003       2004       2005       2006       2007       2008       2009       2010       2011       2012       2013       2014 {c |}     Total
{hline 11}{c +}{hline 132}{c +}{hline 10}
         0 {c |}{res}     3,387      3,446      3,530      3,558      3,539      3,580      3,585      3,578      3,538      3,580      3,577      3,602 {txt}{c |}{res}    96,940 
{txt}         1 {c |}{res}       147        107         98         71         70         42         44         40         32         29         53         28 {txt}{c |}{res}     3,824 
{txt}{hline 11}{c +}{hline 132}{c +}{hline 10}
     Total {c |}{res}     3,534      3,553      3,628      3,629      3,609      3,622      3,629      3,618      3,570      3,609      3,630      3,630 {txt}{c |}{res}   100,764 


{txt}switch_cp_ {c |}                          Year
      lepa {c |}      2015       2016       2017       2018       2019 {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}     3,572      3,633      3,622      3,576      3,599 {txt}{c |}{res}    96,940 
{txt}         1 {c |}{res}        43         21         32         88         29 {txt}{c |}{res}     3,824 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}     3,615      3,654      3,654      3,664      3,628 {txt}{c |}{res}   100,764 
{txt}
{com}.         gen switch_year = switch_cp_lepa*wua_year
{txt}
{com}.         tab switch_year

{txt}switch_year {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}     96,940       96.20       96.20
{txt}       1992 {c |}{res}         87        0.09       96.29
{txt}       1993 {c |}{res}         40        0.04       96.33
{txt}       1994 {c |}{res}         48        0.05       96.38
{txt}       1995 {c |}{res}         63        0.06       96.44
{txt}       1996 {c |}{res}         73        0.07       96.51
{txt}       1997 {c |}{res}        932        0.92       97.44
{txt}       1998 {c |}{res}        531        0.53       97.97
{txt}       1999 {c |}{res}        285        0.28       98.25
{txt}       2000 {c |}{res}        447        0.44       98.69
{txt}       2001 {c |}{res}        178        0.18       98.87
{txt}       2002 {c |}{res}        166        0.16       99.03
{txt}       2003 {c |}{res}        147        0.15       99.18
{txt}       2004 {c |}{res}        107        0.11       99.29
{txt}       2005 {c |}{res}         98        0.10       99.38
{txt}       2006 {c |}{res}         71        0.07       99.45
{txt}       2007 {c |}{res}         70        0.07       99.52
{txt}       2008 {c |}{res}         42        0.04       99.56
{txt}       2009 {c |}{res}         44        0.04       99.61
{txt}       2010 {c |}{res}         40        0.04       99.65
{txt}       2011 {c |}{res}         32        0.03       99.68
{txt}       2012 {c |}{res}         29        0.03       99.71
{txt}       2013 {c |}{res}         53        0.05       99.76
{txt}       2014 {c |}{res}         28        0.03       99.79
{txt}       2015 {c |}{res}         43        0.04       99.83
{txt}       2016 {c |}{res}         21        0.02       99.85
{txt}       2017 {c |}{res}         32        0.03       99.88
{txt}       2018 {c |}{res}         88        0.09       99.97
{txt}       2019 {c |}{res}         29        0.03      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}    100,764      100.00
{txt}
{com}.         by WR_GROUP: egen cohort_cp_lepa = max(switch_year), missing
{txt}
{com}.         tab cohort_cp_lepa

{txt}cohort_cp_l {c |}
        epa {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      2,981        2.96        2.96
{txt}       1992 {c |}{res}      2,245        2.23        5.19
{txt}       1993 {c |}{res}      1,068        1.06        6.25
{txt}       1994 {c |}{res}      1,244        1.23        7.48
{txt}       1995 {c |}{res}      1,609        1.60        9.08
{txt}       1996 {c |}{res}      1,912        1.90       10.98
{txt}       1997 {c |}{res}     24,866       24.68       35.65
{txt}       1998 {c |}{res}     14,247       14.14       49.79
{txt}       1999 {c |}{res}      7,309        7.25       57.05
{txt}       2000 {c |}{res}     11,619       11.53       68.58
{txt}       2001 {c |}{res}      4,674        4.64       73.21
{txt}       2002 {c |}{res}      4,405        4.37       77.59
{txt}       2003 {c |}{res}      3,756        3.73       81.31
{txt}       2004 {c |}{res}      2,735        2.71       84.03
{txt}       2005 {c |}{res}      2,562        2.54       86.57
{txt}       2006 {c |}{res}      1,774        1.76       88.33
{txt}       2007 {c |}{res}      1,726        1.71       90.04
{txt}       2008 {c |}{res}        976        0.97       91.01
{txt}       2009 {c |}{res}      1,150        1.14       92.15
{txt}       2010 {c |}{res}        944        0.94       93.09
{txt}       2011 {c |}{res}        800        0.79       93.88
{txt}       2012 {c |}{res}        645        0.64       94.52
{txt}       2013 {c |}{res}      1,226        1.22       95.74
{txt}       2014 {c |}{res}        595        0.59       96.33
{txt}       2015 {c |}{res}        734        0.73       97.06
{txt}       2016 {c |}{res}        410        0.41       97.47
{txt}       2017 {c |}{res}        502        0.50       97.97
{txt}       2018 {c |}{res}      1,595        1.58       99.55
{txt}       2019 {c |}{res}        455        0.45      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}    100,764      100.00
{txt}
{com}.         *Check if some reverted from lepa to cp, but we didn't see it, and then later they switched from cp to lepa
.                 gen false_nyt = 0
{txt}
{com}.                 replace false_nyt = 1 if type_system_4==1 & wua_year<cohort_cp_lepa
{txt}(270 real changes made)

{com}.                 replace false_nyt = 1 if type_system_3==1 & cohort_cp_lepa>0 & wua_year>=cohort_cp_lepa
{txt}(79 real changes made)

{com}.                 bysort WR_GROUP: egen false_contr = max(false_nyt)      
{txt}
{com}.                 drop if false_contr==1
{txt}(1,740 observations deleted)

{com}.                 xtdescribe //3989 wrgs

{txt}WR_GROUP:  {res}4{txt}, {res}5{txt}, ..., {res}21871                                  {txt}n ={res}       3989
{txt}wua_year:  {res}1991, 1992, ..., 2019                             {txt}T ={res}         29
           {txt}Delta(wua_year) = {res}1 unit
           {txt}Span(wua_year)  = {res}29 periods
           {txt}(WR_GROUP*wua_year uniquely identifies each observation)

Distribution of T_i:   min      5%     25%       50%       75%     95%     max
                    {res}     1       7      24        28        29      29      29

{txt}{col 6}Freq.  Percent    Cum. {c |}  Pattern
 {hline 27}{c +}{c -}{c -}{hline 29}
{res}     1691     42.39   42.39{txt} {c |}  {res}11111111111111111111111111111
       80      2.01   44.40{txt} {c |}  {res}.1111111111111111111111111111
       72      1.80   46.20{txt} {c |}  {res}.....111111111111111111111111
       55      1.38   47.58{txt} {c |}  {res}....1111111111111111111111111
       45      1.13   48.71{txt} {c |}  {res}......11111111111111111111111
       42      1.05   49.76{txt} {c |}  {res}111111111111.1111111111111111
       40      1.00   50.76{txt} {c |}  {res}11111111111.11111111111111111
       39      0.98   51.74{txt} {c |}  {res}...11111111111111111111111111
       39      0.98   52.72{txt} {c |}  {res}1.111111111111111111111111111
     1886     47.28  100.00{txt} {c |} (other patterns)
 {hline 27}{c +}{c -}{c -}{hline 29}
{res}     3989    100.00        {txt} {c |}  {res}XXXXXXXXXXXXXXXXXXXXXXXXXXXXX
{txt}
{com}.                 
.         *Create an indicator of treatment status
.         gen treat_status = type_system_4
{txt}
{com}.                 *Check that it's working right 
.                 tab treat_status if cohort_cp_lepa>0 & wua_year>=cohort_cp_lepa

{txt}treat_statu {c |}
          s {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}     67,427      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     67,427      100.00
{txt}
{com}.                 tab type_system_3 if treat_status==1

     {txt}(mean) {c |}
type_system {c |}
         _3 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}     67,427      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     67,427      100.00
{txt}
{com}. 
. ********************* Table 2 - CP to LEPA column ******************************
.         *Check final number of control and treated WR_GROUPS
.         distinct(WR_GROUP) /*  3989 total WR_GROUPS remaining */

{txt}{col 11}{c |}        Observations
{col 11}{c |}      total   distinct
{hline 10}{c +}{hline 22}
 WR_GROUP {c |}  {res}    99024       3989
{txt}
{com}.         distinct(WR_GROUP) if num_switch_cp_lepa==1 & cohort_cp_lepa > 0 /* 3716 who with an observable switch from cp to lepa */

{txt}{col 11}{c |}        Observations
{col 11}{c |}      total   distinct
{hline 10}{c +}{hline 22}
 WR_GROUP {c |}  {res}    96043       3716
{txt}
{com}.                 distinct(WR_GROUP) if cohort_cp_lepa>0 /* Just double checking this is also 3716 */

{txt}{col 11}{c |}        Observations
{col 11}{c |}      total   distinct
{hline 10}{c +}{hline 22}
 WR_GROUP {c |}  {res}    96043       3716
{txt}
{com}.         distinct(WR_GROUP) if cohort_cp_lepa==0 /* Only 273 pure controls */

{txt}{col 11}{c |}        Observations
{col 11}{c |}      total   distinct
{hline 10}{c +}{hline 22}
 WR_GROUP {c |}  {res}     2981        273
{txt}
{com}.                 
.         /* Create new variable that is the acre-feet applied in the earliest year recorded for each WRG */
.         bysort WR_GROUP: egen min_year = min(wua_year)
{txt}
{com}.         bysort WR_GROUP: gen init_af_used = af_used_irr if min_year==wua_year
{txt}(95,035 missing values generated)

{com}.         bysort WR_GROUP: egen init_af_used_2 = min(init_af_used)
{txt}
{com}.         replace init_af_used = init_af_used_2
{txt}(95,035 real changes made)

{com}.         drop init_af_used_2
{txt}
{com}. 
.         /* Export for using in R */
.         export delimited using "$dr_temp\cp_lepa_prepped.csv", replace
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\cp_lepa_prepped.csv} saved

{com}. 
. ********* CP to LEPA - TWFE estimates for Figures 3, 4, and 5 ******************
.         *Compare to two-way fixed effects
.         xtset WR_GROUP wua_year
{res}
{col 1}{txt:Panel variable: }{res:WR_GROUP}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:wua_year}{txt:, }{res:{bind:1991}}{txt: to }{res:{bind:2019}}{txt:, but with gaps}{p_end}
{txt}{col 10}Delta: {res}1 unit
{txt}
{com}.         xtdescribe

{txt}WR_GROUP:  {res}4{txt}, {res}5{txt}, ..., {res}21871                                  {txt}n ={res}       3989
{txt}wua_year:  {res}1991, 1992, ..., 2019                             {txt}T ={res}         29
           {txt}Delta(wua_year) = {res}1 unit
           {txt}Span(wua_year)  = {res}29 periods
           {txt}(WR_GROUP*wua_year uniquely identifies each observation)

Distribution of T_i:   min      5%     25%       50%       75%     95%     max
                    {res}     1       7      24        28        29      29      29

{txt}{col 6}Freq.  Percent    Cum. {c |}  Pattern
 {hline 27}{c +}{c -}{c -}{hline 29}
{res}     1691     42.39   42.39{txt} {c |}  {res}11111111111111111111111111111
       80      2.01   44.40{txt} {c |}  {res}.1111111111111111111111111111
       72      1.80   46.20{txt} {c |}  {res}.....111111111111111111111111
       55      1.38   47.58{txt} {c |}  {res}....1111111111111111111111111
       45      1.13   48.71{txt} {c |}  {res}......11111111111111111111111
       42      1.05   49.76{txt} {c |}  {res}111111111111.1111111111111111
       40      1.00   50.76{txt} {c |}  {res}11111111111.11111111111111111
       39      0.98   51.74{txt} {c |}  {res}...11111111111111111111111111
       39      0.98   52.72{txt} {c |}  {res}1.111111111111111111111111111
     1886     47.28  100.00{txt} {c |} (other patterns)
 {hline 27}{c +}{c -}{c -}{hline 29}
{res}     3989    100.00        {txt} {c |}  {res}XXXXXXXXXXXXXXXXXXXXXXXXXXXXX
{txt}
{com}.         putexcel set "$dr_temp\twfe.xlsx", ///
>                 sheet("cptolepa_91_19") modify
{res}{txt}
{com}.         putexcel A1 = "dep_var"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

{com}.         putexcel B1 = "effect estimated"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

{com}.         putexcel C1 = "estimate"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

{com}.         putexcel D1 = "se_estimate"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

{com}.         putexcel E1 = "lb_estimate"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

{com}.         putexcel F1 = "ub_estimate"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

{com}.         putexcel G1 = "panel_mean_dep_var"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

{com}.         putexcel H1 = "scaled_estimate"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

{com}.         putexcel I1 = "scaled_lb"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

{com}.         putexcel J1 = "scaled_ub"
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

{com}.         local i = 2
{txt}
{com}.         foreach y in "af_used"  "acres_irr" "depth_applied" {c -(}
{txt}  2{com}.                 putexcel A`i' = "`y'"
{txt}  3{com}.                 putexcel B`i' = "effect_average"
{txt}  4{com}.                 sum `y'
{txt}  5{com}.                 local var_mean = r(mean)
{txt}  6{com}.                         putexcel G`i' = `var_mean'
{txt}  7{com}.                 xtreg `y' treat_status ///
>                         jan_april_mean_ppt may_sep_mean_ppt jan_april_mean_ET0_elev may_sep_mean_ET0_elev i.wua_year ///
>                         , fe vce(cluster WR_GROUP)
{txt}  8{com}.                 putexcel C`i' = _b[treat_status]
{txt}  9{com}.                 putexcel D`i' = _se[treat_status]
{txt} 10{com}.                 local ++i
{txt} 11{com}.         {c )-}
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}af_used {c |}{res}     99,024    196.1752    158.5203        .01       1430
{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved
{res}
{txt}Fixed-effects (within) regression{col 49}Number of obs{col 67}={col 69}{res}    93,354
{txt}Group variable: {res}WR_GROUP{txt}{col 49}Number of groups{col 67}={col 69}{res}     3,775

{txt}R-squared:{col 49}Obs per group:
     Within  = {res}0.1495{col 63}{txt}min{col 67}={col 69}{res}         1
{txt}     Between = {res}0.1938{col 63}{txt}avg{col 67}={col 69}{res}      24.7
{txt}     Overall = {res}0.1436{col 63}{txt}max{col 67}={col 69}{res}        29

{txt}{col 49}F({res}33{txt},{res}3774{txt}){col 67}={col 70}{res}   275.19
{txt}corr(u_i, Xb) = {res}0.2099{txt}{col 49}Prob > F{col 67}={col 73}{res}0.0000

{txt}{ralign 89:(Std. err. adjusted for {res:3,775} clusters in {res:WR_GROUP})}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                af_used{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}treat_status {c |}{col 25}{res}{space 2} 5.894292{col 37}{space 2} 1.317203{col 48}{space 1}    4.47{col 57}{space 3}0.000{col 65}{space 4} 3.311793{col 78}{space 3}  8.47679
{txt}{space 5}jan_april_mean_ppt {c |}{col 25}{res}{space 2}-.4851388{col 37}{space 2} .0306022{col 48}{space 1}  -15.85{col 57}{space 3}0.000{col 65}{space 4}-.5451372{col 78}{space 3}-.4251404
{txt}{space 7}may_sep_mean_ppt {c |}{col 25}{res}{space 2}-.6033667{col 37}{space 2} .0162495{col 48}{space 1}  -37.13{col 57}{space 3}0.000{col 65}{space 4}-.6352253{col 78}{space 3}-.5715081
{txt}jan_april_mean_ET0_elev {c |}{col 25}{res}{space 2} 26.01812{col 37}{space 2} 4.049199{col 48}{space 1}    6.43{col 57}{space 3}0.000{col 65}{space 4} 18.07929{col 78}{space 3} 33.95695
{txt}{space 2}may_sep_mean_ET0_elev {c |}{col 25}{res}{space 2}  18.7133{col 37}{space 2} 3.901446{col 48}{space 1}    4.80{col 57}{space 3}0.000{col 65}{space 4} 11.06415{col 78}{space 3} 26.36245
{txt}{space 23} {c |}
{space 15}wua_year {c |}
{space 18}1992  {c |}{col 25}{res}{space 2}-43.00835{col 37}{space 2} 2.361098{col 48}{space 1}  -18.22{col 57}{space 3}0.000{col 65}{space 4}-47.63751{col 78}{space 3} -38.3792
{txt}{space 18}1993  {c |}{col 25}{res}{space 2}-11.71904{col 37}{space 2} 4.128994{col 48}{space 1}   -2.84{col 57}{space 3}0.005{col 65}{space 4}-19.81432{col 78}{space 3}-3.623766
{txt}{space 18}1994  {c |}{col 25}{res}{space 2}-18.98653{col 37}{space 2} 1.848725{col 48}{space 1}  -10.27{col 57}{space 3}0.000{col 65}{space 4}-22.61112{col 78}{space 3}-15.36193
{txt}{space 18}1995  {c |}{col 25}{res}{space 2}-2.945976{col 37}{space 2}  2.55613{col 48}{space 1}   -1.15{col 57}{space 3}0.249{col 65}{space 4}-7.957505{col 78}{space 3} 2.065553
{txt}{space 18}1996  {c |}{col 25}{res}{space 2} -23.4697{col 37}{space 2} 2.481809{col 48}{space 1}   -9.46{col 57}{space 3}0.000{col 65}{space 4}-28.33551{col 78}{space 3}-18.60388
{txt}{space 18}1997  {c |}{col 25}{res}{space 2}-34.22751{col 37}{space 2} 2.571273{col 48}{space 1}  -13.31{col 57}{space 3}0.000{col 65}{space 4}-39.26873{col 78}{space 3} -29.1863
{txt}{space 18}1998  {c |}{col 25}{res}{space 2}-23.64328{col 37}{space 2} 3.161185{col 48}{space 1}   -7.48{col 57}{space 3}0.000{col 65}{space 4}-29.84107{col 78}{space 3}-17.44548
{txt}{space 18}1999  {c |}{col 25}{res}{space 2}-29.19528{col 37}{space 2} 2.416123{col 48}{space 1}  -12.08{col 57}{space 3}0.000{col 65}{space 4}-33.93232{col 78}{space 3}-24.45825
{txt}{space 18}2000  {c |}{col 25}{res}{space 2}-15.34276{col 37}{space 2} 2.695449{col 48}{space 1}   -5.69{col 57}{space 3}0.000{col 65}{space 4}-20.62743{col 78}{space 3}-10.05808
{txt}{space 18}2001  {c |}{col 25}{res}{space 2} -8.51073{col 37}{space 2} 2.994532{col 48}{space 1}   -2.84{col 57}{space 3}0.005{col 65}{space 4}-14.38179{col 78}{space 3}-2.639672
{txt}{space 18}2002  {c |}{col 25}{res}{space 2}-4.342909{col 37}{space 2} 2.372038{col 48}{space 1}   -1.83{col 57}{space 3}0.067{col 65}{space 4}-8.993509{col 78}{space 3} .3076917
{txt}{space 18}2003  {c |}{col 25}{res}{space 2}-5.195083{col 37}{space 2} 2.600403{col 48}{space 1}   -2.00{col 57}{space 3}0.046{col 65}{space 4}-10.29342{col 78}{space 3}-.0967514
{txt}{space 18}2004  {c |}{col 25}{res}{space 2}-23.74153{col 37}{space 2} 2.675326{col 48}{space 1}   -8.87{col 57}{space 3}0.000{col 65}{space 4}-28.98675{col 78}{space 3} -18.4963
{txt}{space 18}2005  {c |}{col 25}{res}{space 2}-33.70332{col 37}{space 2} 2.809623{col 48}{space 1}  -12.00{col 57}{space 3}0.000{col 65}{space 4}-39.21185{col 78}{space 3} -28.1948
{txt}{space 18}2006  {c |}{col 25}{res}{space 2}-33.29686{col 37}{space 2} 2.669395{col 48}{space 1}  -12.47{col 57}{space 3}0.000{col 65}{space 4}-38.53046{col 78}{space 3}-28.06327
{txt}{space 18}2007  {c |}{col 25}{res}{space 2}-15.51577{col 37}{space 2} 3.377393{col 48}{space 1}   -4.59{col 57}{space 3}0.000{col 65}{space 4}-22.13746{col 78}{space 3}-8.894076
{txt}{space 18}2008  {c |}{col 25}{res}{space 2}-23.69792{col 37}{space 2} 2.983628{col 48}{space 1}   -7.94{col 57}{space 3}0.000{col 65}{space 4} -29.5476{col 78}{space 3}-17.84824
{txt}{space 18}2009  {c |}{col 25}{res}{space 2} -36.9187{col 37}{space 2} 2.784205{col 48}{space 1}  -13.26{col 57}{space 3}0.000{col 65}{space 4}-42.37739{col 78}{space 3}   -31.46
{txt}{space 18}2010  {c |}{col 25}{res}{space 2}-11.44838{col 37}{space 2} 3.410508{col 48}{space 1}   -3.36{col 57}{space 3}0.001{col 65}{space 4}-18.13499{col 78}{space 3}-4.761758
{txt}{space 18}2011  {c |}{col 25}{res}{space 2}-4.856897{col 37}{space 2} 2.875872{col 48}{space 1}   -1.69{col 57}{space 3}0.091{col 65}{space 4}-10.49531{col 78}{space 3} .7815166
{txt}{space 18}2012  {c |}{col 25}{res}{space 2}-15.21937{col 37}{space 2} 2.556022{col 48}{space 1}   -5.95{col 57}{space 3}0.000{col 65}{space 4}-20.23069{col 78}{space 3}-10.20805
{txt}{space 18}2013  {c |}{col 25}{res}{space 2}-9.134845{col 37}{space 2} 3.395497{col 48}{space 1}   -2.69{col 57}{space 3}0.007{col 65}{space 4}-15.79203{col 78}{space 3}-2.477657
{txt}{space 18}2014  {c |}{col 25}{res}{space 2}-23.78118{col 37}{space 2} 3.402741{col 48}{space 1}   -6.99{col 57}{space 3}0.000{col 65}{space 4}-30.45257{col 78}{space 3}-17.10979
{txt}{space 18}2015  {c |}{col 25}{res}{space 2} -31.5405{col 37}{space 2} 2.946565{col 48}{space 1}  -10.70{col 57}{space 3}0.000{col 65}{space 4}-37.31751{col 78}{space 3}-25.76349
{txt}{space 18}2016  {c |}{col 25}{res}{space 2}-39.22323{col 37}{space 2} 2.733039{col 48}{space 1}  -14.35{col 57}{space 3}0.000{col 65}{space 4}-44.58161{col 78}{space 3}-33.86485
{txt}{space 18}2017  {c |}{col 25}{res}{space 2}-35.73775{col 37}{space 2}  2.81954{col 48}{space 1}  -12.68{col 57}{space 3}0.000{col 65}{space 4}-41.26572{col 78}{space 3}-30.20978
{txt}{space 18}2018  {c |}{col 25}{res}{space 2}-52.78524{col 37}{space 2} 3.101815{col 48}{space 1}  -17.02{col 57}{space 3}0.000{col 65}{space 4}-58.86664{col 78}{space 3}-46.70384
{txt}{space 18}2019  {c |}{col 25}{res}{space 2}-40.23556{col 37}{space 2} 3.898931{col 48}{space 1}  -10.32{col 57}{space 3}0.000{col 65}{space 4}-47.87978{col 78}{space 3}-32.59135
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2}  113.052{col 37}{space 2}  23.9346{col 48}{space 1}    4.72{col 57}{space 3}0.000{col 65}{space 4} 66.12602{col 78}{space 3}  159.978
{txt}{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                sigma_u {c |} {res} 151.85729
                {txt}sigma_e {c |} {res} 67.444109
                    {txt}rho {c |} {res} .83524752{txt}   (fraction of variance due to u_i)
{hline 24}{c BT}{hline 64}
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}acres_irr {c |}{res}     99,024    174.1776    110.2795          4        910
{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved
{res}
{txt}Fixed-effects (within) regression{col 49}Number of obs{col 67}={col 69}{res}    93,354
{txt}Group variable: {res}WR_GROUP{txt}{col 49}Number of groups{col 67}={col 69}{res}     3,775

{txt}R-squared:{col 49}Obs per group:
     Within  = {res}0.0015{col 63}{txt}min{col 67}={col 69}{res}         1
{txt}     Between = {res}0.0019{col 63}{txt}avg{col 67}={col 69}{res}      24.7
{txt}     Overall = {res}0.0010{col 63}{txt}max{col 67}={col 69}{res}        29

{txt}{col 49}F({res}33{txt},{res}3774{txt}){col 67}={col 70}{res}     2.45
{txt}corr(u_i, Xb) = {res}0.0223{txt}{col 49}Prob > F{col 67}={col 73}{res}0.0000

{txt}{ralign 89:(Std. err. adjusted for {res:3,775} clusters in {res:WR_GROUP})}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}              acres_irr{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}treat_status {c |}{col 25}{res}{space 2}-.4755306{col 37}{space 2} .6866315{col 48}{space 1}   -0.69{col 57}{space 3}0.489{col 65}{space 4}-1.821735{col 78}{space 3} .8706741
{txt}{space 5}jan_april_mean_ppt {c |}{col 25}{res}{space 2}-.0317021{col 37}{space 2} .0158655{col 48}{space 1}   -2.00{col 57}{space 3}0.046{col 65}{space 4}-.0628079{col 78}{space 3}-.0005962
{txt}{space 7}may_sep_mean_ppt {c |}{col 25}{res}{space 2}-.0062559{col 37}{space 2} .0067795{col 48}{space 1}   -0.92{col 57}{space 3}0.356{col 65}{space 4}-.0195478{col 78}{space 3} .0070359
{txt}jan_april_mean_ET0_elev {c |}{col 25}{res}{space 2} .2409778{col 37}{space 2} 1.922147{col 48}{space 1}    0.13{col 57}{space 3}0.900{col 65}{space 4} -3.52757{col 78}{space 3} 4.009525
{txt}{space 2}may_sep_mean_ET0_elev {c |}{col 25}{res}{space 2}-2.154518{col 37}{space 2} 1.847158{col 48}{space 1}   -1.17{col 57}{space 3}0.244{col 65}{space 4}-5.776042{col 78}{space 3} 1.467007
{txt}{space 23} {c |}
{space 15}wua_year {c |}
{space 18}1992  {c |}{col 25}{res}{space 2}-2.116261{col 37}{space 2} .9650449{col 48}{space 1}   -2.19{col 57}{space 3}0.028{col 65}{space 4}-4.008321{col 78}{space 3}-.2242005
{txt}{space 18}1993  {c |}{col 25}{res}{space 2}-.5530589{col 37}{space 2} 1.923721{col 48}{space 1}   -0.29{col 57}{space 3}0.774{col 65}{space 4}-4.324692{col 78}{space 3} 3.218574
{txt}{space 18}1994  {c |}{col 25}{res}{space 2}-.4591173{col 37}{space 2} .7334066{col 48}{space 1}   -0.63{col 57}{space 3}0.531{col 65}{space 4}-1.897029{col 78}{space 3} .9787944
{txt}{space 18}1995  {c |}{col 25}{res}{space 2}-1.161094{col 37}{space 2} 1.131596{col 48}{space 1}   -1.03{col 57}{space 3}0.305{col 65}{space 4}-3.379693{col 78}{space 3} 1.057504
{txt}{space 18}1996  {c |}{col 25}{res}{space 2}-.6849121{col 37}{space 2}  1.03509{col 48}{space 1}   -0.66{col 57}{space 3}0.508{col 65}{space 4}-2.714302{col 78}{space 3} 1.344478
{txt}{space 18}1997  {c |}{col 25}{res}{space 2} 1.387621{col 37}{space 2} 1.211788{col 48}{space 1}    1.15{col 57}{space 3}0.252{col 65}{space 4}-.9882023{col 78}{space 3} 3.763444
{txt}{space 18}1998  {c |}{col 25}{res}{space 2} 2.458928{col 37}{space 2}   1.4994{col 48}{space 1}    1.64{col 57}{space 3}0.101{col 65}{space 4}-.4807852{col 78}{space 3} 5.398642
{txt}{space 18}1999  {c |}{col 25}{res}{space 2} 2.878566{col 37}{space 2} 1.114835{col 48}{space 1}    2.58{col 57}{space 3}0.010{col 65}{space 4} .6928288{col 78}{space 3} 5.064303
{txt}{space 18}2000  {c |}{col 25}{res}{space 2} 4.800102{col 37}{space 2}  1.30531{col 48}{space 1}    3.68{col 57}{space 3}0.000{col 65}{space 4} 2.240921{col 78}{space 3} 7.359284
{txt}{space 18}2001  {c |}{col 25}{res}{space 2} 3.786451{col 37}{space 2} 1.409508{col 48}{space 1}    2.69{col 57}{space 3}0.007{col 65}{space 4} 1.022979{col 78}{space 3} 6.549923
{txt}{space 18}2002  {c |}{col 25}{res}{space 2} 3.535901{col 37}{space 2} 1.103555{col 48}{space 1}    3.20{col 57}{space 3}0.001{col 65}{space 4} 1.372278{col 78}{space 3} 5.699523
{txt}{space 18}2003  {c |}{col 25}{res}{space 2} 2.862389{col 37}{space 2} 1.201513{col 48}{space 1}    2.38{col 57}{space 3}0.017{col 65}{space 4} .5067122{col 78}{space 3} 5.218067
{txt}{space 18}2004  {c |}{col 25}{res}{space 2} 2.981067{col 37}{space 2} 1.200129{col 48}{space 1}    2.48{col 57}{space 3}0.013{col 65}{space 4} .6281022{col 78}{space 3} 5.334031
{txt}{space 18}2005  {c |}{col 25}{res}{space 2} 2.823382{col 37}{space 2} 1.303895{col 48}{space 1}    2.17{col 57}{space 3}0.030{col 65}{space 4} .2669744{col 78}{space 3} 5.379789
{txt}{space 18}2006  {c |}{col 25}{res}{space 2} 1.999999{col 37}{space 2} 1.190948{col 48}{space 1}    1.68{col 57}{space 3}0.093{col 65}{space 4}-.3349649{col 78}{space 3} 4.334964
{txt}{space 18}2007  {c |}{col 25}{res}{space 2} 2.736309{col 37}{space 2} 1.576713{col 48}{space 1}    1.74{col 57}{space 3}0.083{col 65}{space 4}-.3549831{col 78}{space 3} 5.827601
{txt}{space 18}2008  {c |}{col 25}{res}{space 2} 1.674422{col 37}{space 2} 1.415427{col 48}{space 1}    1.18{col 57}{space 3}0.237{col 65}{space 4}-1.100653{col 78}{space 3} 4.449497
{txt}{space 18}2009  {c |}{col 25}{res}{space 2} 1.448152{col 37}{space 2} 1.329651{col 48}{space 1}    1.09{col 57}{space 3}0.276{col 65}{space 4}-1.158753{col 78}{space 3} 4.055057
{txt}{space 18}2010  {c |}{col 25}{res}{space 2} 1.303828{col 37}{space 2} 1.666964{col 48}{space 1}    0.78{col 57}{space 3}0.434{col 65}{space 4}-1.964409{col 78}{space 3} 4.572064
{txt}{space 18}2011  {c |}{col 25}{res}{space 2}  2.60256{col 37}{space 2} 1.329214{col 48}{space 1}    1.96{col 57}{space 3}0.050{col 65}{space 4}-.0034878{col 78}{space 3} 5.208607
{txt}{space 18}2012  {c |}{col 25}{res}{space 2} 2.582034{col 37}{space 2} 1.194222{col 48}{space 1}    2.16{col 57}{space 3}0.031{col 65}{space 4} .2406507{col 78}{space 3} 4.923417
{txt}{space 18}2013  {c |}{col 25}{res}{space 2} 1.979119{col 37}{space 2} 1.682391{col 48}{space 1}    1.18{col 57}{space 3}0.240{col 65}{space 4}-1.319364{col 78}{space 3} 5.277601
{txt}{space 18}2014  {c |}{col 25}{res}{space 2} .8297305{col 37}{space 2}  1.75426{col 48}{space 1}    0.47{col 57}{space 3}0.636{col 65}{space 4}-2.609658{col 78}{space 3} 4.269119
{txt}{space 18}2015  {c |}{col 25}{res}{space 2} 1.198241{col 37}{space 2} 1.463358{col 48}{space 1}    0.82{col 57}{space 3}0.413{col 65}{space 4}-1.670809{col 78}{space 3} 4.067291
{txt}{space 18}2016  {c |}{col 25}{res}{space 2} 1.509049{col 37}{space 2} 1.266102{col 48}{space 1}    1.19{col 57}{space 3}0.233{col 65}{space 4}-.9732613{col 78}{space 3}  3.99136
{txt}{space 18}2017  {c |}{col 25}{res}{space 2} 2.284555{col 37}{space 2} 1.357376{col 48}{space 1}    1.68{col 57}{space 3}0.092{col 65}{space 4}-.3767062{col 78}{space 3} 4.945816
{txt}{space 18}2018  {c |}{col 25}{res}{space 2}-.4182131{col 37}{space 2}   1.5461{col 48}{space 1}   -0.27{col 57}{space 3}0.787{col 65}{space 4}-3.449485{col 78}{space 3} 2.613058
{txt}{space 18}2019  {c |}{col 25}{res}{space 2} .8063685{col 37}{space 2} 2.001014{col 48}{space 1}    0.40{col 57}{space 3}0.687{col 65}{space 4}-3.116805{col 78}{space 3} 4.729542
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} 185.9666{col 37}{space 2} 12.01805{col 48}{space 1}   15.47{col 57}{space 3}0.000{col 65}{space 4} 162.4041{col 78}{space 3} 209.5291
{txt}{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                sigma_u {c |} {res} 119.28456
                {txt}sigma_e {c |} {res} 30.210412
                    {txt}rho {c |} {res}  .9397239{txt}   (fraction of variance due to u_i)
{hline 24}{c BT}{hline 64}
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved
{res}{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
depth_appl~d {c |}{res}     99,024    1.117894    .4615188   .0000769    2.47476
{txt}file {bf:C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\data\intermediate\twfe.xlsx} saved
{res}
{txt}Fixed-effects (within) regression{col 49}Number of obs{col 67}={col 69}{res}    93,354
{txt}Group variable: {res}WR_GROUP{txt}{col 49}Number of groups{col 67}={col 69}{res}     3,775

{txt}R-squared:{col 49}Obs per group:
     Within  = {res}0.2181{col 63}{txt}min{col 67}={col 69}{res}         1
{txt}     Between = {res}0.1048{col 63}{txt}avg{col 67}={col 69}{res}      24.7
{txt}     Overall = {res}0.1877{col 63}{txt}max{col 67}={col 69}{res}        29

{txt}{col 49}F({res}33{txt},{res}3774{txt}){col 67}={col 70}{res}   590.77
{txt}corr(u_i, Xb) = {res}0.0145{txt}{col 49}Prob > F{col 67}={col 73}{res}0.0000

{txt}{ralign 89:(Std. err. adjusted for {res:3,775} clusters in {res:WR_GROUP})}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}          depth_applied{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}treat_status {c |}{col 25}{res}{space 2} .0314615{col 37}{space 2} .0062861{col 48}{space 1}    5.00{col 57}{space 3}0.000{col 65}{space 4}  .019137{col 78}{space 3}  .043786
{txt}{space 5}jan_april_mean_ppt {c |}{col 25}{res}{space 2}-.0028425{col 37}{space 2} .0001538{col 48}{space 1}  -18.48{col 57}{space 3}0.000{col 65}{space 4}-.0031441{col 78}{space 3}-.0025409
{txt}{space 7}may_sep_mean_ppt {c |}{col 25}{res}{space 2}-.0042614{col 37}{space 2} .0000781{col 48}{space 1}  -54.56{col 57}{space 3}0.000{col 65}{space 4}-.0044145{col 78}{space 3}-.0041082
{txt}jan_april_mean_ET0_elev {c |}{col 25}{res}{space 2} .1704235{col 37}{space 2}  .017252{col 48}{space 1}    9.88{col 57}{space 3}0.000{col 65}{space 4} .1365993{col 78}{space 3} .2042477
{txt}{space 2}may_sep_mean_ET0_elev {c |}{col 25}{res}{space 2} .0962421{col 37}{space 2} .0182366{col 48}{space 1}    5.28{col 57}{space 3}0.000{col 65}{space 4} .0604875{col 78}{space 3} .1319967
{txt}{space 23} {c |}
{space 15}wua_year {c |}
{space 18}1992  {c |}{col 25}{res}{space 2}-.2723716{col 37}{space 2} .0110118{col 48}{space 1}  -24.73{col 57}{space 3}0.000{col 65}{space 4}-.2939613{col 78}{space 3}-.2507819
{txt}{space 18}1993  {c |}{col 25}{res}{space 2}-.0641781{col 37}{space 2} .0176743{col 48}{space 1}   -3.63{col 57}{space 3}0.000{col 65}{space 4}-.0988301{col 78}{space 3} -.029526
{txt}{space 18}1994  {c |}{col 25}{res}{space 2}-.1061028{col 37}{space 2}  .009471{col 48}{space 1}  -11.20{col 57}{space 3}0.000{col 65}{space 4}-.1246715{col 78}{space 3}-.0875341
{txt}{space 18}1995  {c |}{col 25}{res}{space 2}-.0110502{col 37}{space 2}  .011654{col 48}{space 1}   -0.95{col 57}{space 3}0.343{col 65}{space 4} -.033899{col 78}{space 3} .0117986
{txt}{space 18}1996  {c |}{col 25}{res}{space 2}-.1330573{col 37}{space 2} .0112285{col 48}{space 1}  -11.85{col 57}{space 3}0.000{col 65}{space 4}-.1550718{col 78}{space 3}-.1110428
{txt}{space 18}1997  {c |}{col 25}{res}{space 2}-.2252486{col 37}{space 2} .0113555{col 48}{space 1}  -19.84{col 57}{space 3}0.000{col 65}{space 4}-.2475122{col 78}{space 3}-.2029851
{txt}{space 18}1998  {c |}{col 25}{res}{space 2}-.1394516{col 37}{space 2} .0145402{col 48}{space 1}   -9.59{col 57}{space 3}0.000{col 65}{space 4} -.167959{col 78}{space 3}-.1109442
{txt}{space 18}1999  {c |}{col 25}{res}{space 2}-.1701175{col 37}{space 2} .0111686{col 48}{space 1}  -15.23{col 57}{space 3}0.000{col 65}{space 4}-.1920147{col 78}{space 3}-.1482204
{txt}{space 18}2000  {c |}{col 25}{res}{space 2}-.1224584{col 37}{space 2} .0127581{col 48}{space 1}   -9.60{col 57}{space 3}0.000{col 65}{space 4}-.1474719{col 78}{space 3}-.0974449
{txt}{space 18}2001  {c |}{col 25}{res}{space 2}-.0374406{col 37}{space 2} .0141454{col 48}{space 1}   -2.65{col 57}{space 3}0.008{col 65}{space 4} -.065174{col 78}{space 3}-.0097072
{txt}{space 18}2002  {c |}{col 25}{res}{space 2} -.074713{col 37}{space 2} .0110253{col 48}{space 1}   -6.78{col 57}{space 3}0.000{col 65}{space 4}-.0963291{col 78}{space 3}-.0530969
{txt}{space 18}2003  {c |}{col 25}{res}{space 2}-.0236674{col 37}{space 2} .0122222{col 48}{space 1}   -1.94{col 57}{space 3}0.053{col 65}{space 4}-.0476303{col 78}{space 3} .0002954
{txt}{space 18}2004  {c |}{col 25}{res}{space 2} -.153769{col 37}{space 2} .0120325{col 48}{space 1}  -12.78{col 57}{space 3}0.000{col 65}{space 4}-.1773599{col 78}{space 3}-.1301781
{txt}{space 18}2005  {c |}{col 25}{res}{space 2}-.1800515{col 37}{space 2} .0127469{col 48}{space 1}  -14.13{col 57}{space 3}0.000{col 65}{space 4} -.205043{col 78}{space 3}  -.15506
{txt}{space 18}2006  {c |}{col 25}{res}{space 2} -.213179{col 37}{space 2} .0124635{col 48}{space 1}  -17.10{col 57}{space 3}0.000{col 65}{space 4}-.2376148{col 78}{space 3}-.1887432
{txt}{space 18}2007  {c |}{col 25}{res}{space 2}-.1013714{col 37}{space 2} .0150344{col 48}{space 1}   -6.74{col 57}{space 3}0.000{col 65}{space 4}-.1308478{col 78}{space 3}-.0718951
{txt}{space 18}2008  {c |}{col 25}{res}{space 2}-.1732816{col 37}{space 2} .0128607{col 48}{space 1}  -13.47{col 57}{space 3}0.000{col 65}{space 4}-.1984962{col 78}{space 3} -.148067
{txt}{space 18}2009  {c |}{col 25}{res}{space 2}-.2248532{col 37}{space 2} .0128131{col 48}{space 1}  -17.55{col 57}{space 3}0.000{col 65}{space 4}-.2499745{col 78}{space 3}-.1997319
{txt}{space 18}2010  {c |}{col 25}{res}{space 2}-.0500735{col 37}{space 2} .0146559{col 48}{space 1}   -3.42{col 57}{space 3}0.001{col 65}{space 4}-.0788077{col 78}{space 3}-.0213394
{txt}{space 18}2011  {c |}{col 25}{res}{space 2}-.0417271{col 37}{space 2} .0135678{col 48}{space 1}   -3.08{col 57}{space 3}0.002{col 65}{space 4}-.0683281{col 78}{space 3}-.0151262
{txt}{space 18}2012  {c |}{col 25}{res}{space 2}-.1084435{col 37}{space 2} .0129603{col 48}{space 1}   -8.37{col 57}{space 3}0.000{col 65}{space 4}-.1338533{col 78}{space 3}-.0830337
{txt}{space 18}2013  {c |}{col 25}{res}{space 2}-.0652472{col 37}{space 2} .0144591{col 48}{space 1}   -4.51{col 57}{space 3}0.000{col 65}{space 4}-.0935955{col 78}{space 3}-.0368989
{txt}{space 18}2014  {c |}{col 25}{res}{space 2}-.1389246{col 37}{space 2} .0140748{col 48}{space 1}   -9.87{col 57}{space 3}0.000{col 65}{space 4}-.1665196{col 78}{space 3}-.1113297
{txt}{space 18}2015  {c |}{col 25}{res}{space 2}-.1485105{col 37}{space 2} .0128444{col 48}{space 1}  -11.56{col 57}{space 3}0.000{col 65}{space 4}-.1736931{col 78}{space 3}-.1233279
{txt}{space 18}2016  {c |}{col 25}{res}{space 2}-.2232736{col 37}{space 2} .0125528{col 48}{space 1}  -17.79{col 57}{space 3}0.000{col 65}{space 4}-.2478845{col 78}{space 3}-.1986627
{txt}{space 18}2017  {c |}{col 25}{res}{space 2} -.166709{col 37}{space 2} .0133926{col 48}{space 1}  -12.45{col 57}{space 3}0.000{col 65}{space 4}-.1929664{col 78}{space 3}-.1404516
{txt}{space 18}2018  {c |}{col 25}{res}{space 2}-.2732997{col 37}{space 2} .0128729{col 48}{space 1}  -21.23{col 57}{space 3}0.000{col 65}{space 4}-.2985382{col 78}{space 3}-.2480612
{txt}{space 18}2019  {c |}{col 25}{res}{space 2}-.1737444{col 37}{space 2}  .015933{col 48}{space 1}  -10.90{col 57}{space 3}0.000{col 65}{space 4}-.2049825{col 78}{space 3}-.1425064
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .6862618{col 37}{space 2} .0997646{col 48}{space 1}    6.88{col 57}{space 3}0.000{col 65}{space 4}  .490664{col 78}{space 3} .8818597
{txt}{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                sigma_u {c |} {res} .30731643
                {txt}sigma_e {c |} {res} .31980905
                    {txt}rho {c |} {res} .48008746{txt}   (fraction of variance due to u_i)
{hline 24}{c BT}{hline 64}
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{com}. 
. *Now do event study
.         // Create variable showing time relative to treatment for eventdd
.         gen t_to = wua_year - cohort_cp_lepa  
{txt}
{com}.         replace t_to = . if cohort_cp_lepa==0
{txt}(2,981 real changes made, 2,981 to missing)

{com}.         putexcel set "$dr_temp\twfe_event_study.xlsx", ///
>                 sheet("cptolepa_91_19") modify
{res}{txt}
{com}.         putexcel A1 = "stata_name"
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{com}.         putexcel B1 = "ci_low_95"
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{com}.         putexcel C1 = "estimate"
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{com}.         putexcel D1 = "ci_high_95"
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{com}.         putexcel E1 = "effect_estimated"
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{com}.         putexcel F1 = "dep_var"
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{com}.         putexcel G1 = "panel_mean_dep_var"
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{com}.         local i = 0
{txt}
{com}.         foreach y in "af_used"  "acres_irr" "depth_applied" {c -(}
{txt}  2{com}.                 local starter = 2 + `i'*56
{txt}  3{com}.                 local ender = `starter' + 28
{txt}  4{com}.                 local end_vec = `starter' + 55
{txt}  5{com}.                 *Label the periods being estimated (relative to treatment)
.                         *Pre-treatment
.                         local pre_start = `starter'
{txt}  6{com}.                         local pre_stop = `ender' - 1
{txt}  7{com}.                         local cur_eff = -1
{txt}  8{com}.                         forvalues eff_cell=`pre_start'/`pre_stop' {c -(}
{txt}  9{com}.                                 putexcel E`eff_cell' = `cur_eff'
{txt} 10{com}.                                 local cur_eff = `cur_eff' - 1
{txt} 11{com}.                         {c )-}
{txt} 12{com}.                         *Post-treatment
.                         local post_start = `ender'
{txt} 13{com}.                         local post_stop = `end_vec'
{txt} 14{com}.                         local cur_eff = 0
{txt} 15{com}.                         forvalues eff_cell=`post_start'/`post_stop' {c -(}
{txt} 16{com}.                                 putexcel E`eff_cell' = `cur_eff'
{txt} 17{com}.                                 local cur_eff = `cur_eff' + 1
{txt} 18{com}.                         {c )-}
{txt} 19{com}.                 *Label dependent variable
.                 putexcel F`starter':F`end_vec' = "`y'"
{txt} 20{com}.                 *Add sub-sample mean of dep var
.                 sum `y'
{txt} 21{com}.                 local var_mean = r(mean)
{txt} 22{com}.                         putexcel G`starter':G`end_vec' = `var_mean'
{txt} 23{com}.                 *perform event study and save results
.                         qui eventdd `y' jan_april_mean_ppt may_sep_mean_ppt ///
>                                 jan_april_mean_ET0_elev may_sep_mean_ET0_elev i.wua_year, ///
>                                 timevar(t_to) method(fe, cluster(WR_GROUP)) graph_op(leg(off))          
{txt} 24{com}.                 putexcel A`starter' = matrix(e(leads))
{txt} 25{com}.                 putexcel A`ender' = matrix(e(lags))
{txt} 26{com}.                 local ++i
{txt} 27{com}.         {c )-}
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    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}af_used {c |}{res}     99,024    196.1752    158.5203        .01       1430
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    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}acres_irr {c |}{res}     99,024    174.1776    110.2795          4        910
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    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
depth_appl~d {c |}{res}     99,024    1.117894    .4615188   .0000769    2.47476
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{com}. 
. *Close log file
.         log close 
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Micah\Dropbox\Irrigation technology transition\final revisions for conditional acceptance\replication materials\outputs\logs\twfe_att_es.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}19 May 2024, 20:23:47
{txt}{.-}
{smcl}
{txt}{sf}{ul off}